diff --git a/CMake/CXX11.cmake b/CMake/CXX11.cmake
deleted file mode 100644
index ac2ca53139..0000000000
--- a/CMake/CXX11.cmake
+++ /dev/null
@@ -1,48 +0,0 @@
-# This is cloned from
-# https://github.com/nitroshare/CXX11-CMake-Macros
-# until C++11 support finally hits CMake stable (should be 3.1, I think).
-
-# Copyright (c) 2013 Nathan Osman
-
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-
-# The above copyright notice and this permission notice shall be included in
-# all copies or substantial portions of the Software.
-
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-# THE SOFTWARE.
-
-# Determines whether or not the compiler supports C++11
-macro(check_for_cxx11_compiler _VAR)
-    message(STATUS "Checking for C++11 compiler")
-    set(${_VAR})
-    if((MSVC AND (MSVC14)) OR
-       (CMAKE_COMPILER_IS_GNUCXX AND NOT ${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 4.6) OR
-       (CMAKE_CXX_COMPILER_ID STREQUAL "Clang" AND NOT ${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 3.1) OR
-       (CMAKE_CXX_COMPILER_ID STREQUAL "Intel" AND NOT ${CMAKE_CXX_COMPILER_VERSION} VERSION_LESS 12.0))
-        set(${_VAR} 1)
-        message(STATUS "Checking for C++11 compiler - available")
-    else()
-        message(STATUS "Checking for C++11 compiler - unavailable")
-    endif()
-endmacro()
-
-# Sets the appropriate flag to enable C++11 support
-macro(enable_cxx11)
-  if(CMAKE_COMPILER_IS_GNUCXX OR
-     CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR
-     CMAKE_CXX_COMPILER_ID STREQUAL "Intel")
-    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++0x")
-  endif()
-endmacro()
-
diff --git a/CMake/FindPythonModule.cmake b/CMake/FindPythonModule.cmake
index 60fd7a9f55..73a0b3e180 100644
--- a/CMake/FindPythonModule.cmake
+++ b/CMake/FindPythonModule.cmake
@@ -20,7 +20,7 @@ function(find_python_module module)
     endif ()
     # A module's location is usually a directory, but for binary modules
     # it's a .so file.
-    execute_process(COMMAND "${PYTHON}" "-c" 
+    execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c" 
       "import re, ${module}; print(re.compile('/__init__.py.*').sub('',${module}.__file__))"
       RESULT_VARIABLE _${module}_status
       OUTPUT_VARIABLE _${module}_location
@@ -28,7 +28,7 @@ function(find_python_module module)
     if (NOT _${module}_status)
       # Now we have to check the version.
       if (VERSION_REQ)
-        execute_process(COMMAND "${PYTHON}" "-c"
+        execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
             "import ${module}; from distutils.version import StrictVersion; print(StrictVersion(${module}.__version__) >= StrictVersion('${VERSION_REQ}'));"
             RESULT_VARIABLE _version_status
             OUTPUT_VARIABLE _version_compare
diff --git a/CMakeLists.txt b/CMakeLists.txt
index ea42a24c60..5c5ffbe4a6 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -1,4 +1,4 @@
-cmake_minimum_required(VERSION 2.8.10)
+cmake_minimum_required(VERSION 3.3.2)
 project(mlpack C CXX)
 
 include(CMake/cotire.cmake)
@@ -12,7 +12,16 @@ option(MATLAB_BINDINGS "Compile MATLAB bindings if MATLAB is found." OFF)
 option(TEST_VERBOSE "Run test cases with verbose output." OFF)
 option(BUILD_TESTS "Build tests." ON)
 option(BUILD_CLI_EXECUTABLES "Build command-line executables." ON)
-option(BUILD_PYTHON_BINDINGS "Build Python bindings." ON)
+
+# Currently Python bindings aren't known to build successfully on Windows, so
+# set BUILD_PYTHON_BINDINGS to OFF when the platform is Windows.
+if (WIN32)
+  option(BUILD_PYTHON_BINDINGS "Build Python bindings." OFF)
+  message(WARNING "By default Python bindings are not compiled for Windows because they are not known to work.  Set BUILD_PYTHON_BINDINGS to ON if you want them built.")
+else ()
+  option(BUILD_PYTHON_BINDINGS "Build Python bindings." ON)
+endif()
+
 option(BUILD_SHARED_LIBS
     "Compile shared libraries (if OFF, static libraries are compiled)." ON)
 option(BUILD_WITH_COVERAGE
@@ -24,34 +33,9 @@ option(FORCE_CXX11
 option(USE_OPENMP "If available, use OpenMP for parallelization." ON)
 enable_testing()
 
-# Currently Python bindings aren't known to build successfully on Windows, so
-# set BUILD_PYTHON_BINDINGS to OFF when the platform is Windows.
-if (WIN32)
-  option(BUILD_PYTHON_BINDINGS "Build Python bindings." OFF)
-  message(WARNING "By default Python bindings are not compiled for Windows because they are not known to work.  Set BUILD_PYTHON_BINDINGS to ON if you want them built.")
-endif()
-
-# Ensure that we have a C++11 compiler.  In newer versions of CMake, this is
-# done with target_compile_features() when the mlpack library target is added in
-# src/mlpack/CMakeLists.txt.
-if ((${CMAKE_MAJOR_VERSION} LESS 3 OR
-    (${CMAKE_MAJOR_VERSION} EQUAL 3 AND ${CMAKE_MINOR_VERSION} LESS 1))
-    AND NOT FORCE_CXX11)
-  # Older versions of CMake do not support target_compile_features(), so we have
-  # to use something kind of hacky.
-  include(CMake/CXX11.cmake)
-  check_for_cxx11_compiler(HAS_CXX11)
-  if(NOT HAS_CXX11)
-    message(FATAL_ERROR "No C++11 compiler available!")
-  endif()
-  enable_cxx11()
-else()
-  # set required standard to c++11
-  set(CMAKE_CXX_STANDARD 11)
-  set(CMAKE_CXX_STANDARD_REQUIRED ON)
-endif ()
-# Otherwise, we may have to set the C++11 mode after the mlpack target is
-# defined.
+# Set required standard to c++11
+set(CMAKE_CXX_STANDARD 11)
+set(CMAKE_CXX_STANDARD_REQUIRED ON)
 
 # Include modules in the CMake directory.
 set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} "${CMAKE_SOURCE_DIR}/CMake")
@@ -462,13 +446,13 @@ add_subdirectory(src/mlpack)
 # If we need to keep gitversion.hpp up to date, then make sure the mlpack target
 # depends on it.
 if (USING_GIT STREQUAL "YES")
-  add_dependencies(mlpack mlpack_gitversion)
+  add_dependencies(mlpack_headers mlpack_gitversion)
 endif ()
 
 # Make the mlpack_arma_config target depend on mlpack (we couldn't do this
 # before the add_subdirectory() call because the mlpack target didn't exist
 # before that).
-add_dependencies(mlpack mlpack_arma_config)
+add_dependencies(mlpack_headers mlpack_arma_config)
 
 # Make a target to generate the documentation.  If Doxygen isn't installed, then
 # I guess this option will just be unavailable.
diff --git a/COPYRIGHT.txt b/COPYRIGHT.txt
index 79dcae12ad..61b23a0360 100644
--- a/COPYRIGHT.txt
+++ b/COPYRIGHT.txt
@@ -105,6 +105,7 @@ Copyright:
   Copyright 2018, Yasmine Dumouchel <yasmine.dumouchel@gmail.com>
   Copyright 2018, German Lancioni
   Copyright 2018, Ayush Chamoli
+  Copyright 2018, Tommi Laivamaa <tommi.laivamaa@protonmail.com>
   
 License: BSD-3-clause
   All rights reserved.
diff --git a/HISTORY.md b/HISTORY.md
index 2e0728f347..c2f530f2d2 100644
--- a/HISTORY.md
+++ b/HISTORY.md
@@ -1,3 +1,9 @@
+### mlpack 3.0.4
+###### ????-??-??
+  * Bump minimum CMake version to 3.3.2.
+
+  * CMake fixes for Ninja generator by Marc Espie.
+
 ### mlpack 3.0.3
 ###### 2018-07-27
   * Fix Visual Studio compilation issue (#1443).
diff --git a/README.md b/README.md
index 3f39ae086f..d010911173 100644
--- a/README.md
+++ b/README.md
@@ -46,7 +46,7 @@ Python bindings.
 
 ###  1. Introduction
 
-The mlpack website can be found at http://www.mlpack.org and contains numerous
+The mlpack website can be found at http://www.mlpack.org and it contains numerous
 tutorials and extensive documentation.  This README serves as a guide for what
 mlpack is, how to install it, how to run it, and where to find more
 documentation. The website should be consulted for further information:
@@ -84,13 +84,13 @@ mlpack has the following dependencies:
       Armadillo     >= 6.500.0
       Boost (program_options, math_c99, unit_test_framework, serialization,
              spirit)
-      CMake         >= 2.8.5
+      CMake         >= 3.3.2
 
 All of those should be available in your distribution's package manager.  If
 not, you will have to compile each of them by hand.  See the documentation for
 each of those packages for more information.
 
-If you would like use or build the mlpack Python bindings, make sure that the
+If you would like to use or build the mlpack Python bindings, make sure that the
 following Python packages are installed:
 
       setuptools
diff --git a/src/mlpack/CMakeLists.txt b/src/mlpack/CMakeLists.txt
index 29c545c9ee..fa8ce2ac22 100644
--- a/src/mlpack/CMakeLists.txt
+++ b/src/mlpack/CMakeLists.txt
@@ -85,7 +85,8 @@ foreach(incl_file ${INCLUDE_FILES})
   add_custom_command(TARGET mlpack_headers POST_BUILD
     COMMAND ${CMAKE_COMMAND} ARGS -E
       copy ${CMAKE_CURRENT_SOURCE_DIR}/${incl_file}
-           ${CMAKE_BINARY_DIR}/include/mlpack/${incl_file})
+           ${CMAKE_BINARY_DIR}/include/mlpack/${incl_file}
+      BYPRODUCTS ${CMAKE_BINARY_DIR}/include/mlpack/${incl_file})
 endforeach()
 
 # At install time, we simply install that directory of header files we
diff --git a/src/mlpack/bindings/python/CMakeLists.txt b/src/mlpack/bindings/python/CMakeLists.txt
index 18f1ce81cd..bde38e552e 100644
--- a/src/mlpack/bindings/python/CMakeLists.txt
+++ b/src/mlpack/bindings/python/CMakeLists.txt
@@ -15,11 +15,11 @@ endif ()
 
 # Generate Python setuptools file.
 # We can probably use FindPythonInterp when we require CMake 3.0.
-find_program(PYTHON "python" REQUIRED)
-if (NOT PYTHON)
+find_package(PythonInterp)
+if (NOT PYTHON_EXECUTABLE)
   not_found_return("Python not found; not building Python bindings.")
 else ()
-  message(STATUS "Found Python: ${PYTHON}")
+  message(STATUS "Found Python: ${PYTHON_EXECUTABLE}")
 endif ()
 
 # Import find_python_module.
@@ -116,7 +116,8 @@ endforeach()
 add_custom_command(TARGET python_copy PRE_BUILD
     COMMAND ${CMAKE_COMMAND} ARGS -E copy
         ${CMAKE_CURRENT_SOURCE_DIR}/setup.cfg
-        ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/)
+        ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/
+	BYPRODUCTS ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/mlpack/${cython_file})
 add_custom_command(TARGET python_copy PRE_BUILD
     COMMAND ${CMAKE_COMMAND} ARGS -E copy
         ${CMAKE_CURRENT_SOURCE_DIR}/copy_artifacts.py
@@ -127,20 +128,21 @@ if (BUILD_TESTS)
     add_custom_command(TARGET python_copy PRE_BUILD
         COMMAND ${CMAKE_COMMAND} ARGS -E copy
             ${CMAKE_CURRENT_SOURCE_DIR}/${test_file}
-            ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/tests/)
+            ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/tests/
+	BYPRODUCTS ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/tests/${test_file})
   endforeach ()
 endif ()
 
 # Install any dependencies via setuptools automatically.
 add_custom_command(TARGET python_copy POST_BUILD
-    COMMAND ${CMAKE_COMMAND} -E env NO_BUILD=1 ${PYTHON}
+    COMMAND ${CMAKE_COMMAND} -E env NO_BUILD=1 ${PYTHON_EXECUTABLE}
         ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/setup.py build
     WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/)
 
 # Then do the actual build.
 add_custom_command(TARGET python POST_BUILD
-    COMMAND ${PYTHON} ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/setup.py
-        build_ext
+    COMMAND ${PYTHON_EXECUTABLE}
+        ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/setup.py build_ext
     DEPENDS mlpack/arma_numpy.pxd
             mlpack/arma_numpy.pyx
             mlpack/arma.pxd
@@ -153,19 +155,19 @@ add_custom_command(TARGET python POST_BUILD
 
 # Copy the built artifacts, so that it is also an in-place build.
 add_custom_command(TARGET python POST_BUILD
-    COMMAND ${PYTHON}
+    COMMAND ${PYTHON_EXECUTABLE}
         ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/copy_artifacts.py
     WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/)
 
 add_dependencies(python python_copy)
 
 # Configure installation script file.
-execute_process(COMMAND ${PYTHON}
+execute_process(COMMAND ${PYTHON_EXECUTABLE}
     "${CMAKE_CURRENT_SOURCE_DIR}/print_python_version.py" "${CMAKE_INSTALL_PREFIX}"
     OUTPUT_VARIABLE NEW_PYTHONPATH)
 install(CODE "set(ENV{PYTHONPATH} ${NEW_PYTHONPATH})")
 install(CODE "execute_process(COMMAND mkdir -p $ENV{DESTDIR}${NEW_PYTHONPATH})")
-install(CODE "execute_process(COMMAND ${PYTHON}
+install(CODE "execute_process(COMMAND ${PYTHON_EXECUTABLE}
     \"${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/setup.py\" install
     --prefix=${CMAKE_INSTALL_PREFIX} --root=$ENV{DESTDIR}
     WORKING_DIRECTORY \"${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/\")")
@@ -205,7 +207,8 @@ if (BUILD_PYTHON_BINDINGS)
   # enforce it here.  Although this will always be rebuilt, that's okay because
   # distutils will determine whether or not it *actually* needs to be rebuilt.
   add_custom_target(build_pyx_${name}
-      ${PYTHON} ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/setup.py
+      ${PYTHON_EXECUTABLE}
+          ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/setup.py
           build_ext --module=${name}.pyx
       DEPENDS generate_pyx_${name}
       WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/
@@ -225,8 +228,8 @@ endmacro ()
 # Add a test.
 if (BUILD_PYTHON_BINDINGS)
   add_test(NAME python_bindings_test
-      COMMAND ${PYTHON} ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/setup.py
-          test
+      COMMAND ${PYTHON_EXECUTABLE}
+          ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/setup.py test
       WORKING_DIRECTORY ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/)
   set_tests_properties(python_bindings_test
       PROPERTIES ENVIRONMENT "NO_BUILD=1;LD_LIBRARY_PATH=$ENV{LD_LIBRARY_PATH}:${CMAKE_BINARY_DIR}/lib/")
diff --git a/src/mlpack/core.hpp b/src/mlpack/core.hpp
index 9de5b4b4b3..74924248b5 100644
--- a/src/mlpack/core.hpp
+++ b/src/mlpack/core.hpp
@@ -250,6 +250,7 @@
  *   - Shashank Shekhar <contactshashankshekhar@gmail.com>
  *   - Yasmine Dumouchel <yasmine.dumouchel@gmail.com>
  *   - German Lancioni
+ *   - Tommi Laivamaa <tommi.laivamaa@protonmail.com>
  */
 
 // First, include all of the prerequisites.
@@ -293,6 +294,7 @@
 #include <mlpack/core/kernels/pspectrum_string_kernel.hpp>
 #include <mlpack/core/kernels/spherical_kernel.hpp>
 #include <mlpack/core/kernels/triangular_kernel.hpp>
+#include <mlpack/core/kernels/cauchy_kernel.hpp>
 
 // Use OpenMP if compiled with -DHAS_OPENMP.
 #ifdef HAS_OPENMP
diff --git a/src/mlpack/core/kernels/CMakeLists.txt b/src/mlpack/core/kernels/CMakeLists.txt
index 926f61fe8c..fa77bd7beb 100644
--- a/src/mlpack/core/kernels/CMakeLists.txt
+++ b/src/mlpack/core/kernels/CMakeLists.txt
@@ -1,6 +1,7 @@
 # Define the files we need to compile.
 # Anything not in this list will not be compiled into mlpack.
 set(SOURCES
+  cauchy_kernel.hpp
   cosine_distance.hpp
   cosine_distance_impl.hpp
   epanechnikov_kernel.hpp
diff --git a/src/mlpack/core/kernels/cauchy_kernel.hpp b/src/mlpack/core/kernels/cauchy_kernel.hpp
new file mode 100644
index 0000000000..d7953fb140
--- /dev/null
+++ b/src/mlpack/core/kernels/cauchy_kernel.hpp
@@ -0,0 +1,98 @@
+/**
+ * @file cauchy_kernel.hpp
+ * @author Tommi Laivamaa
+ *
+ * Implementation of the Cauchy kernel (CauchyKernel),
+ *
+ * mlpack is free software; you may redistribute it and/or modify it under the
+ * terms of the 3-clause BSD license.  You should have received a copy of the
+ * 3-clause BSD license along with mlpack.  If not, see
+ * http://www.opensource.org/licenses/BSD-3-Clause for more information.
+ */
+#ifndef MLPACK_CORE_KERNELS_CAUCHY_KERNEL_HPP
+#define MLPACK_CORE_KERNELS_CAUCHY_KERNEL_HPP
+
+#include <mlpack/prereqs.hpp>
+#include <mlpack/core/metrics/lmetric.hpp>
+#include <mlpack/core/kernels/kernel_traits.hpp>
+
+namespace mlpack {
+namespace kernel {
+
+/**
+ * The Cauchy kernel. Given two vector @f$ x @f$, @f$ y @f$, and a bandwidth
+ * @f$ \sigma @f$ (set in the constructor),
+ *
+ * @f[
+ * K(x, y) = \frac{1}{1 + (\frac{|| x - y ||}{\sigma})^2}.
+ * @f]
+ *
+ * For more details, see the following published paper:
+ *
+ * @code
+ * @inproceedings{Basak2008,
+ *   title={A least square kernel machine with box constraints},
+ *   author={Basak, Jayanta},
+ *   booktitle={Pattern Recognition, 2008. ICPR 2008. 19th International
+ *       Conference on},
+ *   pages={1--4},
+ *   year={2008},
+ *   organization={IEEE}
+ * }
+ * @endcode
+ */
+class CauchyKernel
+{
+ public:
+  /**
+   * Construct the Cauchy kernel; by default, the bandwidth is 1.0.
+   */
+  CauchyKernel(double bandwidth = 1.0) : bandwidth(bandwidth)
+  { }
+
+  /**
+   * Evaluation of the Cauchy kernel. This could be generalized to use any
+   * distance metric, not the Euclidean distance, but for now, the Euclidean
+   * distance is used.
+   *
+   * @tparam VecTypeA Type of first vector (arma::vec, arma::sp_vec).
+   * @tparam VecTypeB Type of second vector (arma::vec, arma::sp_vec).
+   * @param a First vector.
+   * @param b Second vector.
+   * @return K(a, b).
+   */
+  template<typename VecTypeA, typename VecTypeB>
+  double Evaluate(const VecTypeA& a, const VecTypeB& b)
+  {
+    return 1 / (1 + (
+        std::pow(metric::EuclideanDistance::Evaluate(a, b) / bandwidth, 2)));
+  }
+
+  /**
+   * Serialize the kernel.
+   */
+  template<typename Archive>
+  void serialize(Archive& ar, const unsigned int /* version */)
+  {
+    ar & BOOST_SERIALIZATION_NVP(bandwidth);
+  }
+
+ private:
+  //! Kernel bandwidth.
+  double bandwidth;
+};
+
+//! Kernel traits for the Cauchy kernel.
+template<>
+class KernelTraits<CauchyKernel>
+{
+ public:
+  //! The Cauchy kernel is normalized: K(x, x) = 1 for all x.
+  static const bool IsNormalized = true;
+};
+
+} // namespace kernel
+} // namespace mlpack
+
+#endif
+ 
diff --git a/src/mlpack/core/tree/binary_space_tree/vantage_point_split_impl.hpp b/src/mlpack/core/tree/binary_space_tree/vantage_point_split_impl.hpp
index fd15b45746..b1714b7c7d 100644
--- a/src/mlpack/core/tree/binary_space_tree/vantage_point_split_impl.hpp
+++ b/src/mlpack/core/tree/binary_space_tree/vantage_point_split_impl.hpp
@@ -25,7 +25,7 @@ SplitNode(const BoundType& bound, MatType& data, const size_t begin,
     const size_t count, SplitInfo& splitInfo)
 {
   ElemType mu = 0;
-  size_t vantagePointIndex;
+  size_t vantagePointIndex = 0;
 
   // Find the best vantage point.
   SelectVantagePoint(bound.Metric(), data, begin, count, vantagePointIndex, mu);
diff --git a/src/mlpack/methods/adaboost/CMakeLists.txt b/src/mlpack/methods/adaboost/CMakeLists.txt
index 5505f057fb..583131ad45 100644
--- a/src/mlpack/methods/adaboost/CMakeLists.txt
+++ b/src/mlpack/methods/adaboost/CMakeLists.txt
@@ -1,5 +1,3 @@
-cmake_minimum_required(VERSION 2.8)
-
 # Define the files we need to compile.
 # Anything not in this list will not be compiled into mlpack.
 set(SOURCES
diff --git a/src/mlpack/methods/ann/ffn.hpp b/src/mlpack/methods/ann/ffn.hpp
index 8c9a8356e4..34b4c96660 100644
--- a/src/mlpack/methods/ann/ffn.hpp
+++ b/src/mlpack/methods/ann/ffn.hpp
@@ -28,6 +28,7 @@
 #include "init_rules/network_init.hpp"
 
 #include <mlpack/methods/ann/layer/layer_types.hpp>
+#include <mlpack/methods/ann/layer/layer.hpp>
 #include <mlpack/methods/ann/init_rules/random_init.hpp>
 #include <mlpack/core/optimizers/rmsprop/rmsprop.hpp>
 
@@ -462,6 +463,24 @@ class FFN
 
 } // namespace ann
 } // namespace mlpack
+
+//! Set the serialization version of the FFN class.  Multiple template arguments
+//! makes this ugly...
+namespace boost {
+namespace serialization {
+
+template<typename OutputLayerType,
+         typename InitializationRuleType,
+         typename... CustomLayer>
+struct version<
+    mlpack::ann::FFN<OutputLayerType, InitializationRuleType, CustomLayer...>>
+{
+  BOOST_STATIC_CONSTANT(int, value = 1);
+};
+
+} // namespace serialization
+} // namespace boost
+
 // Include implementation.
 #include "ffn_impl.hpp"
 
diff --git a/src/mlpack/methods/ann/ffn_impl.hpp b/src/mlpack/methods/ann/ffn_impl.hpp
index 9aca45fa5d..878060a4d4 100644
--- a/src/mlpack/methods/ann/ffn_impl.hpp
+++ b/src/mlpack/methods/ann/ffn_impl.hpp
@@ -495,13 +495,20 @@ template<typename OutputLayerType, typename InitializationRuleType,
          typename... CustomLayers>
 template<typename Archive>
 void FFN<OutputLayerType, InitializationRuleType, CustomLayers...>::serialize(
-    Archive& ar, const unsigned int /* version */)
+    Archive& ar, const unsigned int version)
 {
   ar & BOOST_SERIALIZATION_NVP(parameter);
   ar & BOOST_SERIALIZATION_NVP(width);
   ar & BOOST_SERIALIZATION_NVP(height);
   ar & BOOST_SERIALIZATION_NVP(currentInput);
 
+  // Earlier versions of the FFN code did not serialize whether or not the model
+  // was reset.
+  if (version > 0)
+  {
+    ar & BOOST_SERIALIZATION_NVP(reset);
+  }
+
   // Be sure to clear other layers before loading.
   if (Archive::is_loading::value)
   {
@@ -515,7 +522,10 @@ void FFN<OutputLayerType, InitializationRuleType, CustomLayers...>::serialize(
   // If we are loading, we need to initialize the weights.
   if (Archive::is_loading::value)
   {
-    reset = false;
+    // The behavior in earlier versions was to always assume the weights needed
+    // to be reset.
+    if (version == 0)
+      reset = false;
 
     size_t offset = 0;
     for (size_t i = 0; i < network.size(); ++i)
diff --git a/src/mlpack/methods/ann/rnn.hpp b/src/mlpack/methods/ann/rnn.hpp
index 95e457551d..3e5490e1ea 100644
--- a/src/mlpack/methods/ann/rnn.hpp
+++ b/src/mlpack/methods/ann/rnn.hpp
@@ -22,6 +22,7 @@
 #include "init_rules/network_init.hpp"
 
 #include <mlpack/methods/ann/layer/layer_types.hpp>
+#include <mlpack/methods/ann/layer/layer.hpp>
 #include <mlpack/methods/ann/init_rules/random_init.hpp>
 #include <mlpack/core/optimizers/sgd/sgd.hpp>
 
@@ -385,6 +386,23 @@ class RNN
 } // namespace ann
 } // namespace mlpack
 
+//! Set the serialization version of the RNN class.  Multiple template arguments
+//! makes this ugly...
+namespace boost {
+namespace serialization {
+
+template<typename OutputLayerType,
+         typename InitializationRuleType,
+         typename... CustomLayer>
+struct version<
+    mlpack::ann::RNN<OutputLayerType, InitializationRuleType, CustomLayer...>>
+{
+  BOOST_STATIC_CONSTANT(int, value = 1);
+};
+
+} // namespace serialization
+} // namespace boost
+
 // Include implementation.
 #include "rnn_impl.hpp"
 
diff --git a/src/mlpack/methods/ann/rnn_impl.hpp b/src/mlpack/methods/ann/rnn_impl.hpp
index 1dec206daf..0b1567b4ea 100644
--- a/src/mlpack/methods/ann/rnn_impl.hpp
+++ b/src/mlpack/methods/ann/rnn_impl.hpp
@@ -504,7 +504,7 @@ template<typename OutputLayerType, typename InitializationRuleType,
          typename... CustomLayers>
 template<typename Archive>
 void RNN<OutputLayerType, InitializationRuleType, CustomLayers...>::serialize(
-    Archive& ar, const unsigned int /* version */)
+    Archive& ar, const unsigned int version)
 {
   ar & BOOST_SERIALIZATION_NVP(parameter);
   ar & BOOST_SERIALIZATION_NVP(rho);
@@ -513,6 +513,12 @@ void RNN<OutputLayerType, InitializationRuleType, CustomLayers...>::serialize(
   ar & BOOST_SERIALIZATION_NVP(outputSize);
   ar & BOOST_SERIALIZATION_NVP(targetSize);
 
+  // Earlier versions of the RNN code did not serialize the 'reset' variable.
+  if (version > 0)
+  {
+    ar & BOOST_SERIALIZATION_NVP(reset);
+  }
+
   if (Archive::is_loading::value)
   {
     std::for_each(network.begin(), network.end(),
@@ -525,7 +531,10 @@ void RNN<OutputLayerType, InitializationRuleType, CustomLayers...>::serialize(
   // If we are loading, we need to initialize the weights.
   if (Archive::is_loading::value)
   {
-    reset = false;
+    // Earlier versions of the RNN code assumed that the weights needed to be
+    // reset on load.
+    if (version == 0)
+      reset = false;
 
     size_t offset = 0;
     for (LayerTypes<CustomLayers...>& layer : network)
diff --git a/src/mlpack/methods/cf/cf.hpp b/src/mlpack/methods/cf/cf.hpp
index f44517eb94..5154c60d53 100644
--- a/src/mlpack/methods/cf/cf.hpp
+++ b/src/mlpack/methods/cf/cf.hpp
@@ -179,6 +179,9 @@ class CFType
   //! Get the cleaned data matrix.
   const arma::sp_mat& CleanedData() const { return cleanedData; }
 
+  //! Get the normalization object.
+  const NormalizationType& Normalization() const { return normalization; }
+
   /**
    * Generates the given number of recommendations for all users.
    *
diff --git a/src/mlpack/methods/cf/normalization/combined_normalization.hpp b/src/mlpack/methods/cf/normalization/combined_normalization.hpp
index 3bd36a8451..6836707f14 100644
--- a/src/mlpack/methods/cf/normalization/combined_normalization.hpp
+++ b/src/mlpack/methods/cf/normalization/combined_normalization.hpp
@@ -93,7 +93,7 @@ class CombinedNormalization
   /**
    * Return normalizations tuple.
    */
-  TupleType Normalizations() const
+  const TupleType& Normalizations() const
   {
     return normalizations;
   }
diff --git a/src/mlpack/methods/decision_stump/CMakeLists.txt b/src/mlpack/methods/decision_stump/CMakeLists.txt
index e9d9c04c1a..26042b4435 100644
--- a/src/mlpack/methods/decision_stump/CMakeLists.txt
+++ b/src/mlpack/methods/decision_stump/CMakeLists.txt
@@ -1,5 +1,3 @@
-cmake_minimum_required(VERSION 2.8)
-
 # Define the files we need to compile.
 # Anything not in this list will not be compiled into mlpack.
 set(SOURCES
diff --git a/src/mlpack/methods/decision_tree/CMakeLists.txt b/src/mlpack/methods/decision_tree/CMakeLists.txt
index 448e4d62ee..25598133b7 100644
--- a/src/mlpack/methods/decision_tree/CMakeLists.txt
+++ b/src/mlpack/methods/decision_tree/CMakeLists.txt
@@ -1,5 +1,3 @@
-cmake_minimum_required(VERSION 2.8)
-
 # Define the files we need to compile.
 # Anything not in this list will not be compiled into mlpack.
 set(SOURCES
diff --git a/src/mlpack/methods/decision_tree/decision_tree.hpp b/src/mlpack/methods/decision_tree/decision_tree.hpp
index a8108cdd94..b20b33ac25 100644
--- a/src/mlpack/methods/decision_tree/decision_tree.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree.hpp
@@ -56,6 +56,8 @@ class DecisionTree :
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
    * @param data Dataset to train on.
    * @param datasetInfo Type information for each dimension of the dataset.
    * @param labels Labels for each training point.
@@ -64,9 +66,9 @@ class DecisionTree :
    * @param minimumGainSplit Minimum gain for the node to split.
    */
   template<typename MatType, typename LabelsType>
-  DecisionTree(MatType&& data,
+  DecisionTree(MatType data,
                const data::DatasetInfo& datasetInfo,
-               LabelsType&& labels,
+               LabelsType labels,
                const size_t numClasses,
                const size_t minimumLeafSize = 10,
                const double minimumGainSplit = 1e-7);
@@ -77,6 +79,8 @@ class DecisionTree :
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
    * @param data Dataset to train on.
    * @param labels Labels for each training point.
    * @param numClasses Number of classes in the dataset.
@@ -84,8 +88,8 @@ class DecisionTree :
    * @param minimumGainSplit Minimum gain for the node to split.
    */
   template<typename MatType, typename LabelsType>
-  DecisionTree(MatType&& data,
-               LabelsType&& labels,
+  DecisionTree(MatType data,
+               LabelsType labels,
                const size_t numClasses,
                const size_t minimumLeafSize = 10,
                const double minimumGainSplit = 1e-7);
@@ -96,6 +100,9 @@ class DecisionTree :
    * and minimumGainSplit too small may cause the tree to overfit, but setting
    * them too large may cause it to underfit.
    *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
    * @param data Dataset to train on.
    * @param datasetInfo Type information for each dimension of the dataset.
    * @param labels Labels for each training point.
@@ -105,11 +112,11 @@ class DecisionTree :
    * @param minimumGainSplit Minimum gain for the node to split.
    */
   template<typename MatType, typename LabelsType, typename WeightsType>
-  DecisionTree(MatType&& data,
+  DecisionTree(MatType data,
                const data::DatasetInfo& datasetInfo,
-               LabelsType&& labels,
+               LabelsType labels,
                const size_t numClasses,
-               WeightsType&& weights,
+               WeightsType weights,
                const size_t minimumLeafSize = 10,
                const double minimumGainSplit = 1e-7,
                const std::enable_if_t<arma::is_arma_type<
@@ -122,6 +129,9 @@ class DecisionTree :
    * and minimumGainSplit too small may cause the tree to overfit, but setting
    * them too large may cause it to underfit.
    *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
    * @param data Dataset to train on.
    * @param labels Labels for each training point.
    * @param numClasses Number of classes in the dataset.
@@ -130,10 +140,10 @@ class DecisionTree :
    * @param minimumGainSplit Minimum gain for the node to split.
    */
   template<typename MatType, typename LabelsType, typename WeightsType>
-  DecisionTree(MatType&& data,
-               LabelsType&& labels,
+  DecisionTree(MatType data,
+               LabelsType labels,
                const size_t numClasses,
-               WeightsType&& weights,
+               WeightsType weights,
                const size_t minimumLeafSize = 10,
                const double minimumGainSplit = 1e-7,
                const std::enable_if_t<arma::is_arma_type<
@@ -191,6 +201,8 @@ class DecisionTree :
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
    * @param data Dataset to train on.
    * @param datasetInfo Type information for each dimension.
    * @param labels Labels for each training point.
@@ -200,9 +212,9 @@ class DecisionTree :
    * @param minimumGainSplit Minimum gain for the node to split.
    */
   template<typename MatType, typename LabelsType>
-  void Train(MatType&& data,
+  void Train(MatType data,
              const data::DatasetInfo& datasetInfo,
-             LabelsType&& labels,
+             LabelsType labels,
              const size_t numClasses,
              const size_t minimumLeafSize = 10,
              const double minimumGainSplit = 1e-7);
@@ -213,6 +225,8 @@ class DecisionTree :
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
+   * Use std::move if data or labels are no longer needed to avoid copies.
+   *
    * @param data Dataset to train on.
    * @param labels Labels for each training point.
    * @param numClasses Number of classes in the dataset.
@@ -221,8 +235,8 @@ class DecisionTree :
    * @param minimumGainSplit Minimum gain for the node to split.
    */
   template<typename MatType, typename LabelsType>
-  void Train(MatType&& data,
-             LabelsType&& labels,
+  void Train(MatType data,
+             LabelsType labels,
              const size_t numClasses,
              const size_t minimumLeafSize = 10,
              const double minimumGainSplit = 1e-7);
@@ -234,6 +248,9 @@ class DecisionTree :
    * minimumGainSplit too small may cause the tree to overfit, but setting them
    * too large may cause it to underfit.
    *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
    * @param data Dataset to train on.
    * @param datasetInfo Type information for each dimension.
    * @param labels Labels for each training point.
@@ -243,11 +260,11 @@ class DecisionTree :
    * @param minimumGainSplit Minimum gain for the node to split.
    */
   template<typename MatType, typename LabelsType, typename WeightsType>
-  void Train(MatType&& data,
+  void Train(MatType data,
              const data::DatasetInfo& datasetInfo,
-             LabelsType&& labels,
+             LabelsType labels,
              const size_t numClasses,
-             WeightsType&& weights,
+             WeightsType weights,
              const size_t minimumLeafSize = 10,
              const double minimumGainSplit = 1e-7,
              const std::enable_if_t<arma::is_arma_type<typename
@@ -259,6 +276,9 @@ class DecisionTree :
    * minimumLeafSize and minimumGainSplit too small may cause the tree to
    * overfit, but setting them too large may cause it to underfit.
    *
+   * Use std::move if data, labels or weights are no longer needed to avoid
+   * copies.
+   *
    * @param data Dataset to train on.
    * @param labels Labels for each training point.
    * @param numClasses Number of classes in the dataset.
@@ -267,10 +287,10 @@ class DecisionTree :
    * @param minimumGainSplit Minimum gain for the node to split.
    */
   template<typename MatType, typename LabelsType, typename WeightsType>
-  void Train(MatType&& data,
-             LabelsType&& labels,
+  void Train(MatType data,
+             LabelsType labels,
              const size_t numClasses,
-             WeightsType&& weights,
+             WeightsType weights,
              const size_t minimumLeafSize = 10,
              const double minimumGainSplit = 1e-7,
              const std::enable_if_t<arma::is_arma_type<typename
diff --git a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
index 91a0827982..51471c0584 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_impl.hpp
@@ -28,9 +28,9 @@ DecisionTree<FitnessFunction,
              CategoricalSplitType,
              DimensionSelectionType,
              ElemType,
-             NoRecursion>::DecisionTree(MatType&& data,
+             NoRecursion>::DecisionTree(MatType data,
                                         const data::DatasetInfo& datasetInfo,
-                                        LabelsType&& labels,
+                                        LabelsType labels,
                                         const size_t numClasses,
                                         const size_t minimumLeafSize,
                                         const double minimumGainSplit)
@@ -39,8 +39,8 @@ DecisionTree<FitnessFunction,
   using TrueLabelsType = typename std::decay<LabelsType>::type;
 
   // Copy or move data.
-  TrueMatType tmpData(std::forward<MatType>(data));
-  TrueLabelsType tmpLabels(std::forward<LabelsType>(labels));
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
@@ -61,8 +61,8 @@ DecisionTree<FitnessFunction,
              CategoricalSplitType,
              DimensionSelectionType,
              ElemType,
-             NoRecursion>::DecisionTree(MatType&& data,
-                                        LabelsType&& labels,
+             NoRecursion>::DecisionTree(MatType data,
+                                        LabelsType labels,
                                         const size_t numClasses,
                                         const size_t minimumLeafSize,
                                         const double minimumGainSplit)
@@ -71,8 +71,8 @@ DecisionTree<FitnessFunction,
   using TrueLabelsType = typename std::decay<LabelsType>::type;
 
   // Copy or move data.
-  TrueMatType tmpData(std::forward<MatType>(data));
-  TrueLabelsType tmpLabels(std::forward<LabelsType>(labels));
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
@@ -93,11 +93,11 @@ DecisionTree<FitnessFunction,
              CategoricalSplitType,
              DimensionSelectionType,
              ElemType,
-             NoRecursion>::DecisionTree(MatType&& data,
+             NoRecursion>::DecisionTree(MatType data,
                                         const data::DatasetInfo& datasetInfo,
-                                        LabelsType&& labels,
+                                        LabelsType labels,
                                         const size_t numClasses,
-                                        WeightsType&& weights,
+                                        WeightsType weights,
                                         const size_t minimumLeafSize,
                                         const double minimumGainSplit,
                                         const std::enable_if_t<
@@ -110,9 +110,9 @@ DecisionTree<FitnessFunction,
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
-  TrueMatType tmpData(std::forward<MatType>(data));
-  TrueLabelsType tmpLabels(std::forward<LabelsType>(labels));
-  TrueWeightsType tmpWeights(std::forward<WeightsType>(weights));
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
 
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
@@ -132,10 +132,10 @@ DecisionTree<FitnessFunction,
              CategoricalSplitType,
              DimensionSelectionType,
              ElemType,
-             NoRecursion>::DecisionTree(MatType&& data,
-                                        LabelsType&& labels,
+             NoRecursion>::DecisionTree(MatType data,
+                                        LabelsType labels,
                                         const size_t numClasses,
-                                        WeightsType&& weights,
+                                        WeightsType weights,
                                         const size_t minimumLeafSize,
                                         const double minimumGainSplit,
                                         const std::enable_if_t<
@@ -148,9 +148,9 @@ DecisionTree<FitnessFunction,
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
-  TrueMatType tmpData(std::forward<MatType>(data));
-  TrueLabelsType tmpLabels(std::forward<LabelsType>(labels));
-  TrueWeightsType tmpWeights(std::forward<WeightsType>(weights));
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
 
   // Pass off work to the weighted Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
@@ -345,9 +345,9 @@ void DecisionTree<FitnessFunction,
                   CategoricalSplitType,
                   DimensionSelectionType,
                   ElemType,
-                  NoRecursion>::Train(MatType&& data,
+                  NoRecursion>::Train(MatType data,
                                       const data::DatasetInfo& datasetInfo,
-                                      LabelsType&& labels,
+                                      LabelsType labels,
                                       const size_t numClasses,
                                       const size_t minimumLeafSize,
                                       const double minimumGainSplit)
@@ -366,8 +366,8 @@ void DecisionTree<FitnessFunction,
   using TrueLabelsType = typename std::decay<LabelsType>::type;
 
   // Copy or move data.
-  TrueMatType tmpData(std::forward<MatType>(data));
-  TrueLabelsType tmpLabels(std::forward<LabelsType>(labels));
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
@@ -388,8 +388,8 @@ void DecisionTree<FitnessFunction,
                   CategoricalSplitType,
                   DimensionSelectionType,
                   ElemType,
-                  NoRecursion>::Train(MatType&& data,
-                                      LabelsType&& labels,
+                  NoRecursion>::Train(MatType data,
+                                      LabelsType labels,
                                       const size_t numClasses,
                                       const size_t minimumLeafSize,
                                       const double minimumGainSplit)
@@ -408,8 +408,8 @@ void DecisionTree<FitnessFunction,
   using TrueLabelsType = typename std::decay<LabelsType>::type;
 
   // Copy or move data.
-  TrueMatType tmpData(std::forward<MatType>(data));
-  TrueLabelsType tmpLabels(std::forward<LabelsType>(labels));
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
 
   // Pass off work to the Train() method.
   arma::rowvec weights; // Fake weights, not used.
@@ -430,11 +430,11 @@ void DecisionTree<FitnessFunction,
                   CategoricalSplitType,
                   DimensionSelectionType,
                   ElemType,
-                  NoRecursion>::Train(MatType&& data,
+                  NoRecursion>::Train(MatType data,
                                       const data::DatasetInfo& datasetInfo,
-                                      LabelsType&& labels,
+                                      LabelsType labels,
                                       const size_t numClasses,
-                                      WeightsType&& weights,
+                                      WeightsType weights,
                                       const size_t minimumLeafSize,
                                       const double minimumGainSplit,
                                       const std::enable_if_t<arma::is_arma_type<
@@ -456,9 +456,9 @@ void DecisionTree<FitnessFunction,
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
-  TrueMatType tmpData(std::forward<MatType>(data));
-  TrueLabelsType tmpLabels(std::forward<LabelsType>(labels));
-  TrueWeightsType tmpWeights(std::forward<WeightsType>(weights));
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
 
   // Pass off work to the Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, datasetInfo, tmpLabels, numClasses,
@@ -478,10 +478,10 @@ void DecisionTree<FitnessFunction,
                   CategoricalSplitType,
                   DimensionSelectionType,
                   ElemType,
-                  NoRecursion>::Train(MatType&& data,
-                                      LabelsType&& labels,
+                  NoRecursion>::Train(MatType data,
+                                      LabelsType labels,
                                       const size_t numClasses,
-                                      WeightsType&& weights,
+                                      WeightsType weights,
                                       const size_t minimumLeafSize,
                                       const double minimumGainSplit,
                                       const std::enable_if_t<arma::is_arma_type<
@@ -503,9 +503,9 @@ void DecisionTree<FitnessFunction,
   using TrueWeightsType = typename std::decay<WeightsType>::type;
 
   // Copy or move data.
-  TrueMatType tmpData(std::forward<MatType>(data));
-  TrueLabelsType tmpLabels(std::forward<LabelsType>(labels));
-  TrueWeightsType tmpWeights(std::forward<WeightsType>(weights));
+  TrueMatType tmpData(std::move(data));
+  TrueLabelsType tmpLabels(std::move(labels));
+  TrueWeightsType tmpWeights(std::move(weights));
 
   // Pass off work to the Train() method.
   Train<true>(tmpData, 0, tmpData.n_cols, tmpLabels, numClasses, tmpWeights,
diff --git a/src/mlpack/methods/decision_tree/decision_tree_main.cpp b/src/mlpack/methods/decision_tree/decision_tree_main.cpp
index 0e4be96c0c..8369312d1d 100644
--- a/src/mlpack/methods/decision_tree/decision_tree_main.cpp
+++ b/src/mlpack/methods/decision_tree/decision_tree_main.cpp
@@ -180,13 +180,30 @@ static void mlpackMain()
     {
       arma::Row<double> weights =
           std::move(CLI::GetParam<arma::Mat<double>>("weights"));
-      model->tree = DecisionTree<>(trainingSet, model->info, labels,
-          numClasses, weights, minLeafSize, minimumGainSplit);
+      if (CLI::HasParam("print_training_error"))
+      {
+        model->tree = DecisionTree<>(trainingSet, model->info, labels,
+            numClasses, std::move(weights), minLeafSize, minimumGainSplit);
+      }
+      else
+      {
+        model->tree = DecisionTree<>(std::move(trainingSet), model->info,
+            std::move(labels), numClasses, std::move(weights), minLeafSize,
+            minimumGainSplit);
+      }
     }
     else
     {
-      model->tree = DecisionTree<>(trainingSet, model->info, labels,
-          numClasses, minLeafSize, minimumGainSplit);
+      if (CLI::HasParam("print_training_error"))
+      {
+        model->tree = DecisionTree<>(trainingSet, model->info, labels,
+            numClasses, minLeafSize, minimumGainSplit);
+      }
+      else
+      {
+        model->tree = DecisionTree<>(std::move(trainingSet), model->info,
+            std::move(labels), numClasses, minLeafSize, minimumGainSplit);
+      }
     }
 
     // Do we need to print training error?
diff --git a/src/mlpack/methods/mean_shift/mean_shift_impl.hpp b/src/mlpack/methods/mean_shift/mean_shift_impl.hpp
index dabe2980a6..f7e6a1beb2 100644
--- a/src/mlpack/methods/mean_shift/mean_shift_impl.hpp
+++ b/src/mlpack/methods/mean_shift/mean_shift_impl.hpp
@@ -68,7 +68,7 @@ EstimateRadius(const MatType& data, double ratio)
   arma::rowvec maxDistances = max(distances);
 
   // Calculate and return the radius.
-  return sum(maxDistances) / (double) data.n_cols;
+  return arma::sum(maxDistances) / (double) data.n_cols;
 }
 
 // Class to compare two vectors.
@@ -218,14 +218,14 @@ inline void MeanShift<UseKernel, KernelType, MatType>::Cluster(
     // Initial centroid is the seed itself.
     allCentroids.col(i) = pSeeds->unsafe_col(i);
     for (size_t completedIterations = 0; completedIterations < maxIterations
-      || forceConvergence; completedIterations++)
+        || forceConvergence; completedIterations++)
     {
       // Store new centroid in this.
       arma::colvec newCentroid = arma::zeros<arma::colvec>(pSeeds->n_rows);
 
       rangeSearcher.Search(allCentroids.unsafe_col(i), validRadius,
           neighbors, distances);
-      if (neighbors[0].size() <= 1)
+      if (neighbors[0].size() == 0) // There are no points in the cluster.
         break;
 
       // Calculate new centroid.
@@ -265,8 +265,9 @@ inline void MeanShift<UseKernel, KernelType, MatType>::Cluster(
   // forcing convergence, take 1 random centroid calculated.
   if (centroids.empty())
   {
-    Log::Warn << "No clusters converge, setting 1 random centroid calculated. "
-    "Try a larger max_iterations or pass force_convergence flag." << std::endl;
+    Log::Warn << "No clusters converged; setting 1 random centroid calculated. "
+        << "Try increasing the maximum number of iterations or setting the "
+        << "option to force convergence." << std::endl;
 
     if (maxIterations == 0)
     {
diff --git a/src/mlpack/methods/neighbor_search/neighbor_search.hpp b/src/mlpack/methods/neighbor_search/neighbor_search.hpp
index 4d33a225ff..0086c16ffc 100644
--- a/src/mlpack/methods/neighbor_search/neighbor_search.hpp
+++ b/src/mlpack/methods/neighbor_search/neighbor_search.hpp
@@ -187,42 +187,25 @@ class NeighborSearch
 
   /**
    * Set the reference set to a new reference set, and build a tree if
-   * necessary.  This method is called 'Train()' in order to match the rest of
-   * the mlpack abstractions, even though calling this "training" is maybe a bit
-   * of a stretch.
+   * necessary. The dataset is copied by default, but the copy can be avoided by
+   * transferring the ownership of the dataset using std::move().  This method
+   * is called 'Train()' in order to match the rest of the mlpack abstractions,
+   * even though calling this "training" is maybe a bit of a stretch.
    *
    * @param referenceSet New set of reference data.
    */
-  void Train(const MatType& referenceSet);
+  void Train(MatType referenceSet);
 
   /**
-   * Set the reference set to a new reference set, taking ownership of the set,
-   * and build a tree if necessary.  This method is called 'Train()' in order to
-   * match the rest of the mlpack abstractions, even though calling this
-   * "training" is maybe a bit of a stretch.
-   *
-   * @param referenceSet New set of reference data.
-   */
-  void Train(MatType&& referenceSet);
-
-  /**
-   * Set the reference tree as a copy of the given reference tree.
-   *
-   * This method will copy the given tree.  You can avoid this copy by using the
-   * Train() method that takes a rvalue reference to the tree.
+   * Set the reference tree to a new reference tree.  The tree is copied by
+   * default, but the copy can be avoided by using std::move() to transfer the
+   * ownership of the tree.  This method is called 'Train()' in order to match
+   * the rest of the mlpack abstractions, even though calling this "training" is
+   * maybe a bit of a stretch.
    *
    * @param referenceTree Pre-built tree for reference points.
    */
-  void Train(const Tree& referenceTree);
-
-  /**
-   * Set the reference tree to a new reference tree.
-   *
-   * This method will take ownership of the given tree.
-   *
-   * @param referenceTree Pre-built tree for reference points.
-   */
-  void Train(Tree&& referenceTree);
+  void Train(Tree referenceTree);
 
   /**
    * For each point in the query set, compute the nearest neighbors and store
@@ -359,11 +342,6 @@ class NeighborSearch
   //! Reference dataset.  In some situations we may be the owner of this.
   const MatType* referenceSet;
 
-  //! If true, this object created the trees and is responsible for them.
-  bool treeOwner;
-  //! If true, we own the reference set.
-  bool setOwner;
-
   //! Indicates the neighbor search mode.
   NeighborSearchMode searchMode;
   //! Indicates the relative error to be considered in approximate search.
diff --git a/src/mlpack/methods/neighbor_search/neighbor_search_impl.hpp b/src/mlpack/methods/neighbor_search/neighbor_search_impl.hpp
index 4eaf4550ea..d260171b62 100644
--- a/src/mlpack/methods/neighbor_search/neighbor_search_impl.hpp
+++ b/src/mlpack/methods/neighbor_search/neighbor_search_impl.hpp
@@ -63,8 +63,6 @@ SingleTreeTraversalType>::NeighborSearch(MatType referenceSetIn,
         BuildTree<Tree>(std::move(referenceSetIn), oldFromNewReferences)),
     referenceSet(mode == NAIVE_MODE ?  new MatType(std::move(referenceSetIn)) :
         &referenceTree->Dataset()),
-    treeOwner(mode != NAIVE_MODE),
-    setOwner(mode == NAIVE_MODE),
     searchMode(mode),
     epsilon(epsilon),
     metric(metric),
@@ -92,8 +90,6 @@ SingleTreeTraversalType>::NeighborSearch(Tree referenceTree,
                                          const MetricType metric) :
     referenceTree(new Tree(std::move(referenceTree))),
     referenceSet(&this->referenceTree->Dataset()),
-    treeOwner(true),
-    setOwner(false),
     searchMode(mode),
     epsilon(epsilon),
     metric(metric),
@@ -120,8 +116,6 @@ SingleTreeTraversalType>::NeighborSearch(const NeighborSearchMode mode,
                                          const MetricType metric) :
     referenceTree(NULL),
     referenceSet(new MatType()), // Empty matrix.
-    treeOwner(false),
-    setOwner(true),
     searchMode(mode),
     epsilon(epsilon),
     metric(metric),
@@ -136,7 +130,7 @@ SingleTreeTraversalType>::NeighborSearch(const NeighborSearchMode mode,
   if (mode != NAIVE_MODE)
   {
     referenceTree = BuildTree<Tree>(*referenceSet, oldFromNewReferences);
-    treeOwner = true;
+    referenceSet = &referenceTree->Dataset();
   }
 }
 
@@ -155,8 +149,6 @@ SingleTreeTraversalType>::NeighborSearch(const NeighborSearch& other) :
     referenceTree(other.referenceTree ? new Tree(*other.referenceTree) : NULL),
     referenceSet(other.referenceTree ? &referenceTree->Dataset() :
         new MatType(*other.referenceSet)),
-    treeOwner(other.referenceTree),
-    setOwner(!other.referenceTree),
     searchMode(other.searchMode),
     epsilon(other.epsilon),
     metric(other.metric),
@@ -181,8 +173,6 @@ SingleTreeTraversalType>::NeighborSearch(NeighborSearch&& other) :
     oldFromNewReferences(std::move(other.oldFromNewReferences)),
     referenceTree(other.referenceTree),
     referenceSet(other.referenceSet),
-    treeOwner(other.treeOwner),
-    setOwner(other.setOwner),
     searchMode(other.searchMode),
     epsilon(other.epsilon),
     metric(std::move(other.metric)),
@@ -194,8 +184,7 @@ SingleTreeTraversalType>::NeighborSearch(NeighborSearch&& other) :
   other.referenceSet = new MatType();
   other.referenceTree = BuildTree<Tree>(*other.referenceSet,
       other.oldFromNewReferences);
-  other.treeOwner = true;
-  other.setOwner = true;
+  other.referenceSet = &other.referenceTree->Dataset();
   other.searchMode = DUAL_TREE_MODE,
   other.epsilon = 0.0;
   other.baseCases = 0;
@@ -229,17 +218,15 @@ NeighborSearch<SortPolicy,
     return *this; // Nothing to do.
 
   // Clean memory first.
-  if (treeOwner && referenceTree)
+  if (referenceTree)
     delete referenceTree;
-  if (setOwner && referenceSet)
+  else
     delete referenceSet;
 
   oldFromNewReferences = other.oldFromNewReferences;
   referenceTree = other.referenceTree ? new Tree(*other.referenceTree) : NULL;
   referenceSet = other.referenceTree ? &referenceTree->Dataset() :
       new MatType(*other.referenceSet);
-  treeOwner = (other.referenceTree != NULL);
-  setOwner = (other.referenceTree == NULL);
   searchMode = other.searchMode;
   epsilon = other.epsilon;
   metric = other.metric;
@@ -274,16 +261,14 @@ NeighborSearch<SortPolicy,
     return *this; // Nothing to do.
 
   // Clean memory first.
-  if (treeOwner && referenceTree)
+  if (referenceTree)
     delete referenceTree;
-  if (setOwner && referenceSet)
+  else
     delete referenceSet;
 
   oldFromNewReferences = std::move(other.oldFromNewReferences);
   referenceTree = other.referenceTree;
   referenceSet = other.referenceSet;
-  treeOwner = other.treeOwner;
-  setOwner = other.setOwner;
   searchMode = other.searchMode;
   epsilon = other.epsilon;
   metric = other.metric;
@@ -292,11 +277,9 @@ NeighborSearch<SortPolicy,
   treeNeedsReset = other.treeNeedsReset;
 
   // Reset the other object.
-  other.referenceSet = new MatType();
   other.referenceTree = BuildTree<Tree>(*other.referenceSet,
       other.oldFromNewReferences);
-  other.treeOwner = true;
-  other.setOwner = true;
+  other.referenceSet = &other.referenceTree->Dataset();
   other.searchMode = DUAL_TREE_MODE,
   other.epsilon = 0.0;
   other.baseCases = 0;
@@ -316,9 +299,9 @@ template<typename SortPolicy,
 NeighborSearch<SortPolicy, MetricType, MatType, TreeType, DualTreeTraversalType,
 SingleTreeTraversalType>::~NeighborSearch()
 {
-  if (treeOwner && referenceTree)
+  if (referenceTree)
     delete referenceTree;
-  if (setOwner && referenceSet)
+  else
     delete referenceSet;
 }
 
@@ -331,72 +314,30 @@ template<typename SortPolicy,
          template<typename> class DualTreeTraversalType,
          template<typename> class SingleTreeTraversalType>
 void NeighborSearch<SortPolicy, MetricType, MatType, TreeType,
-DualTreeTraversalType, SingleTreeTraversalType>::Train(
-    const MatType& referenceSet)
+DualTreeTraversalType, SingleTreeTraversalType>::Train(MatType referenceSetIn)
 {
   // Clean up the old tree, if we built one.
-  if (treeOwner && referenceTree)
+  if (referenceTree)
   {
     oldFromNewReferences.clear();
     delete referenceTree;
-  }
-
-  // Delete the old reference set, if we owned it.
-  if (setOwner && this->referenceSet)
-    delete this->referenceSet;
-
-  // We may need to rebuild the tree.
-  if (searchMode != NAIVE_MODE)
-  {
-    referenceTree = BuildTree<Tree>(referenceSet, oldFromNewReferences);
-    treeOwner = true;
-    this->referenceSet = &referenceTree->Dataset();
+    referenceTree = NULL;
   }
   else
   {
-    treeOwner = false;
-    this->referenceSet = &referenceSet;
-  }
-
-  setOwner = false; // We don't own the set in either case.
-}
-
-template<typename SortPolicy,
-         typename MetricType,
-         typename MatType,
-         template<typename TreeMetricType,
-                  typename TreeStatType,
-                  typename TreeMatType> class TreeType,
-         template<typename> class DualTreeTraversalType,
-         template<typename> class SingleTreeTraversalType>
-void NeighborSearch<SortPolicy, MetricType, MatType, TreeType,
-DualTreeTraversalType, SingleTreeTraversalType>::Train(MatType&& referenceSetIn)
-{
-  // Clean up the old tree, if we built one.
-  if (treeOwner && referenceTree)
-  {
-    oldFromNewReferences.clear();
-    delete referenceTree;
-  }
-
-  // Delete the old reference set, if we owned it.
-  if (setOwner && referenceSet)
     delete referenceSet;
+  }
 
   // We may need to rebuild the tree.
   if (searchMode != NAIVE_MODE)
   {
     referenceTree = BuildTree<Tree>(std::move(referenceSetIn),
         oldFromNewReferences);
-    treeOwner = true;
     referenceSet = &referenceTree->Dataset();
-    setOwner = false;
   }
   else
   {
-    treeOwner = false;
     referenceSet = new MatType(std::move(referenceSetIn));
-    setOwner = true;
   }
 }
 
@@ -409,56 +350,24 @@ template<typename SortPolicy,
          template<typename> class DualTreeTraversalType,
          template<typename> class SingleTreeTraversalType>
 void NeighborSearch<SortPolicy, MetricType, MatType, TreeType,
-DualTreeTraversalType, SingleTreeTraversalType>::Train(
-    const Tree& referenceTree)
+DualTreeTraversalType, SingleTreeTraversalType>::Train(Tree referenceTree)
 {
   if (searchMode == NAIVE_MODE)
     throw std::invalid_argument("cannot train on given reference tree when "
         "naive search (without trees) is desired");
 
-  if (treeOwner && this->referenceTree)
+  if (this->referenceTree)
   {
     oldFromNewReferences.clear();
     delete this->referenceTree;
   }
-
-  if (setOwner && referenceSet)
-    delete this->referenceSet;
-
-  this->referenceTree = new Tree(referenceTree);
-  this->referenceSet = &this->referenceTree->Dataset();
-  treeOwner = true;
-  setOwner = false;
-}
-
-template<typename SortPolicy,
-         typename MetricType,
-         typename MatType,
-         template<typename TreeMetricType,
-                  typename TreeStatType,
-                  typename TreeMatType> class TreeType,
-         template<typename> class DualTreeTraversalType,
-         template<typename> class SingleTreeTraversalType>
-void NeighborSearch<SortPolicy, MetricType, MatType, TreeType,
-DualTreeTraversalType, SingleTreeTraversalType>::Train(Tree&& referenceTree)
-{
-  if (searchMode == NAIVE_MODE)
-    throw std::invalid_argument("cannot train on given reference tree when "
-        "naive search (without trees) is desired");
-
-  if (treeOwner && this->referenceTree)
+  else
   {
-    oldFromNewReferences.clear();
-    delete this->referenceTree;
-  }
-
-  if (setOwner && referenceSet)
     delete this->referenceSet;
+  }
 
   this->referenceTree = new Tree(std::move(referenceTree));
   this->referenceSet = &this->referenceTree->Dataset();
-  treeOwner = true;
-  setOwner = false;
 }
 
 /**
@@ -1046,12 +955,9 @@ DualTreeTraversalType, SingleTreeTraversalType>::serialize(
   if (searchMode == NAIVE_MODE)
   {
     // Delete the current reference set, if necessary and if we are loading.
-    if (Archive::is_loading::value)
+    if (Archive::is_loading::value && referenceSet)
     {
-      if (setOwner && referenceSet)
-        delete referenceSet;
-
-      setOwner = true; // We will own the reference set when we load it.
+      delete referenceSet;
     }
 
     ar & BOOST_SERIALIZATION_NVP(referenceSet);
@@ -1060,24 +966,19 @@ DualTreeTraversalType, SingleTreeTraversalType>::serialize(
     // If we are loading, set the tree to NULL and clean up memory if necessary.
     if (Archive::is_loading::value)
     {
-      if (treeOwner && referenceTree)
+      if (referenceTree)
         delete referenceTree;
 
       referenceTree = NULL;
       oldFromNewReferences.clear();
-      treeOwner = false;
     }
   }
   else
   {
     // Delete the current reference tree, if necessary and if we are loading.
-    if (Archive::is_loading::value)
+    if (Archive::is_loading::value && referenceTree)
     {
-      if (treeOwner && referenceTree)
-        delete referenceTree;
-
-      // After we load the tree, we will own it.
-      treeOwner = true;
+      delete referenceTree;
     }
 
     ar & BOOST_SERIALIZATION_NVP(referenceTree);
@@ -1087,12 +988,8 @@ DualTreeTraversalType, SingleTreeTraversalType>::serialize(
     // necessary.
     if (Archive::is_loading::value)
     {
-      if (setOwner && referenceSet)
-        delete referenceSet;
-
       referenceSet = &referenceTree->Dataset();
       metric = referenceTree->Metric(); // Get the metric from the tree.
-      setOwner = false;
     }
   }
 
diff --git a/src/mlpack/methods/perceptron/CMakeLists.txt b/src/mlpack/methods/perceptron/CMakeLists.txt
index f0b4afc596..8e6763464d 100644
--- a/src/mlpack/methods/perceptron/CMakeLists.txt
+++ b/src/mlpack/methods/perceptron/CMakeLists.txt
@@ -1,5 +1,3 @@
-cmake_minimum_required(VERSION 2.8)
-
 # Define the files we need to compile.
 # Anything not in this list will not be compiled into mlpack.
 set(SOURCES
diff --git a/src/mlpack/methods/random_forest/CMakeLists.txt b/src/mlpack/methods/random_forest/CMakeLists.txt
index 3ac70de8a8..bfc6121ad6 100644
--- a/src/mlpack/methods/random_forest/CMakeLists.txt
+++ b/src/mlpack/methods/random_forest/CMakeLists.txt
@@ -1,5 +1,3 @@
-cmake_minimum_required(VERSION 2.8)
-
 # Define the files we need to compile.
 # Anything not in this list will not be compiled into mlpack.
 set(SOURCES
diff --git a/src/mlpack/tests/data/thyroid_test.csv b/src/mlpack/tests/data/thyroid_test.csv
index be1aeda632..a8ca35c43a 100644
--- a/src/mlpack/tests/data/thyroid_test.csv
+++ b/src/mlpack/tests/data/thyroid_test.csv
@@ -1,3428 +1,3428 @@
-0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0061,0.028,0.111,0.131,0.085,0,1,0
-0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0013,0.019,0.084,0.078,0.107,0,0,1
-0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.031,0.239,0.1,0.239,0,0,1
-0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.001,0.018,0.087,0.088,0.099,0,0,1
-0.22,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0004,0.022,0.134,0.135,0.099,0,0,1
-0.22,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0016,0.02,0.123,0.113,0.109,0,0,1
-0.39,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0016,0.036,0.133,0.144,0.093,0,0,1
-0.77,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00081,0.02,0.08,0.096,0.08316,0,0,1
-0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00025,0.014,0.113,0.096,0.11746,0,0,1
-0.23,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0026,0.011,0.104,0.104,0.099,0,0,1
-0.45,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.061,0.006,0.023,0.087,0.026,1,0,0
-0.65,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,3e-05,0.023,0.154,0.09,0.17,0,0,1
-0.53,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,4e-05,0.022,0.115,0.093,0.124,0,0,1
-0.79,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.00419,0.011,0.078,0.097,0.081,0,0,1
-0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0032,0.015,0.159,0.116,0.136,0,0,1
-0.57,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0011,0.0201,0.12,0.1,0.12,0,0,1
-0.54,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,2e-05,0.0201,0.225,0.083,0.273,0,0,1
-0.61,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0007,0.022,0.118,0.096,0.12266,0,0,1
-0.61,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,4e-05,0.0201,0.115,0.083,0.138,0,0,1
-0.14,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0024,0.0201,0.089,0.074,0.121,0,0,1
-0.57,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0011,0.0201,0.103,0.092,0.112,0,0,1
-0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.001,0.028,0.122,0.112,0.109,0,0,1
-0.59,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0077,0.018,0.111,0.107,0.104,0,1,0
-0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6e-05,0.016,0.123,0.081,0.152,0,0,1
-0.73,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00089,0.019,0.151,0.097,0.155,0,0,1
-0.6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2e-05,0.0201,0.175,0.103,0.169,0,0,1
-0.56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.027,0.017,0.074,0.101,0.074,0,1,0
-0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.019,0.012,0.102,0.095,0.10669,0,1,0
-0.61,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0059,0.02,0.082,0.096,0.08523,0,0,1
-0.42,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.00232,0.0201,0.125,0.112,0.112,0,0,1
-0.78,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00859,0.0201,0.124,0.098,0.126,0,0,1
-0.64,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,3e-05,0.0201,0.065,0.082,0.08,0,0,1
-0.52,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0201,0.16,0.082,0.195,0,0,1
-0.81,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0019,0.019,0.118,0.102,0.116,0,0,1
-0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0025,0.014,0.109,0.073,0.148,0,0,1
-0.75,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0.0052,0.0201,0.096,0.089,0.108,0,0,1
-0.64,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.00038,0.0201,0.1,0.084,0.119,0,0,1
-0.23,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.002,0.0201,0.136,0.067,0.203,0,0,1
-0.21,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0015,0.0201,0.14,0.137,0.102,0,0,1
-0.53,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.00012,0.0201,0.047,0.093,0.05,0,0,1
-0.39,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.0011,0.016,0.133,0.102,0.131,0,0,1
-0.39,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0045,0.017,0.171,0.146,0.117,0,0,1
-0.45,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0014,0.022,0.141,0.098,0.144,0,0,1
-0.35,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0026,0.025,0.161,0.126,0.128,0,0,1
-0.36,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.001,0.018,0.101,0.092,0.11,0,0,1
-0.36,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0012,0.026,0.148,0.129,0.115,0,0,1
-0.51,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00208,0.019,0.063,0.058,0.107,0,0,1
-0.29,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0019,0.0208,0.096,0.102,0.096,0,0,1
-0.76,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00232,0.026,0.105,0.096,0.10914,0,0,1
-0.81,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.003,0.007,0.069,0.096,0.07172,0,0,1
-0.7,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.0014,0.014,0.071,0.08,0.089,0,0,1
-0.87,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0019,0.018,0.119,0.089,0.134,0,0,1
-0.63,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00232,0.015,0.116,0.088,0.132,0,0,1
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diff --git a/src/mlpack/tests/data/thyroid_train.csv b/src/mlpack/tests/data/thyroid_train.csv
index 007248f5df..c79b6110b4 100644
--- a/src/mlpack/tests/data/thyroid_train.csv
+++ b/src/mlpack/tests/data/thyroid_train.csv
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+0.29,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00058,0.0206,0.08,0.082,0.098,3
+0.57,1,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0.05,0.013,0.064,0.099,0.06451,3
+0.57,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0008,0.0206,0.082,0.099,0.083,3
+0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0013,0.0206,0.161,0.133,0.121,3
+0.2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00208,0.0206,0.105,0.104,0.101,3
+0.55,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00034,0.014,0.123,0.096,0.128,3
+0.25,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0.0011,0.0206,0.101,0.107,0.094,3
+0.25,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.014,0.0206,0.081,0.09,0.09,3
+0.71,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1e-05,0.0206,0.146,0.093,0.156,3
+0.26,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0034,0.0206,0.104,0.112,0.092,3
+0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0019,0.0206,0.096,0.087,0.11,3
+0.58,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.0018,0.0206,0.063,0.086,0.073,3
+0.57,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0014,0.0206,0.082,0.107,0.077,3
+0.35,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.0039,0.0206,0.083,0.085,0.097,3
+0.53,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4e-05,0.018,0.102,0.1,0.102,3
+0.35,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,4e-05,0.0208,0.071,0.11,0.064,3
+0.6,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00189,0.0206,0.11118,0.099,0.11207,3
+0.38,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0025,0.0206,0.081,0.086,0.094,3
+0.79,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0014,0.006,0.094,0.099,0.09475,3
+0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0025,0.015,0.081,0.086,0.094,3
+0.48,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00189,0.0206,0.091,0.091,0.1,3
+0.45,1,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.00208,0.0206,0.093,0.116,0.08,3
+0.64,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.0013,0.024,0.111,0.115,0.097,3
+0.64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0043,0.0206,0.114,0.104,0.108,3
+0.57,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.00208,0.0206,0.076,0.1,0.075,3
+0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00013,0.01,0.102,0.082,0.124,3
+0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0018,0.0206,0.084,0.095,0.088,3
+0.55,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0024,0.023,0.104,0.103,0.101,3
+0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0048,0.0206,0.087,0.093,0.093,3
+0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.004,0.032,0.106,0.087,0.122,3
+0.39,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.0016,0.012,0.095,0.093,0.102,3
+0.45,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,6e-05,0.046,0.21,0.111,0.19,3
+0.45,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0.00044,0.028,0.153,0.091,0.168,3
+0.54,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0014,0.0206,0.098,0.101,0.097,3
+0.46,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,5e-05,0.022,0.097,0.077,0.125,3
+0.33,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0015,0.0208,0.104,0.085,0.122,3
+0.7,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0027,0.0206,0.155,0.104,0.148,3
+0.07,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0024,0.0206,0.155,0.094,0.165,3
+0.38,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,6e-05,0.018,0.139,0.099,0.14012,3
+0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0031,0.016,0.086,0.104,0.083,3
+0.27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013,0.0174,0.087,0.085,0.103,2
+0.58,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0055,0.0208,0.082,0.092,0.089,3
+0.58,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0026,0.0206,0.164,0.11,0.149,3
+0.49,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.116,0.007,0.072,0.095,0.076,3
+0.82,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.0066,0.016,0.092,0.1,0.091,2
+0.18,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00073,0.0206,0.194,0.097,0.2,3
+0.33,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.00016,0.052,0.183,0.083,0.22,3
+0.33,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4e-05,0.0208,0.159,0.101,0.157,3
+0.37,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0028,0.0206,0.11118,0.099,0.11207,3
+0.55,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00189,0.0208,0.11118,0.099,0.11207,3
+0.72,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0.00189,0.0206,0.114,0.111,0.102,3
+0.77,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0035,0.0206,0.114,0.1,0.115,3
+0.62,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00208,0.017,0.096,0.099,0.09677,3
+0.53,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,5e-05,0.022,0.089,0.083,0.107,3
+0.69,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0014,0.0206,0.123,0.091,0.136,3
+0.76,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0034,0.0206,0.077,0.067,0.115,3
+0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00189,0.0206,0.098,0.099,0.09879,3
+0.38,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0.0011,0.022,0.145,0.123,0.118,3
+0.3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.003,0.0206,0.096,0.082,0.118,3
+0.68,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0.0019,0.0206,0.093,0.086,0.108,3
+0.31,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0034,0.0206,0.104,0.094,0.112,3
+0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0052,0.0206,0.095,0.111,0.085,3
+0.35,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0018,0.017,0.118,0.11,0.107,3
+0.54,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00075,0.023,0.109,0.114,0.095,3
+0.43,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0.0017,0.0206,0.104,0.091,0.115,3
+0.16,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00189,0.025,0.071,0.08,0.088,3
+0.16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0097,0.0174,0.098,0.13,0.076,2
+0.16,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.0062,0.0174,0.108,0.114,0.094,2
+0.45,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.0206,0.132,0.09,0.147,3
+0.32,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0014,0.0206,0.108,0.115,0.094,3
+0.36,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00074,0.015,0.072,0.05,0.145,3
+0.6,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013,0.014,0.057,0.062,0.092,3
+0.37,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00208,0.024,0.177,0.146,0.122,3
+0.26,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,8e-05,0.017,0.174,0.123,0.142,3
+0.3,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,8e-05,0.019,0.147,0.103,0.143,3
+0.5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.0033,0.0208,0.107,0.107,0.1,3
+0.59,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1e-05,0.02,0.113,0.086,0.131,3
+0.72,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.005,0.0206,0.101,0.088,0.115,3
+0.65,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00047,0.008,0.154,0.075,0.204,3
+0.77,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6e-05,0.0206,0.125,0.107,0.117,3
+0.41,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0013,0.025,0.125,0.114,0.109,3
+0.88,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.013,0.0174,0.123,0.099,0.124,2
+0.64,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00078,0.0206,0.106,0.088,0.121,3
+0.46,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.00189,0.0206,0.093,0.091,0.102,3
diff --git a/src/mlpack/tests/feedforward_network_test.cpp b/src/mlpack/tests/feedforward_network_test.cpp
index 60f325aa13..0d691737ea 100644
--- a/src/mlpack/tests/feedforward_network_test.cpp
+++ b/src/mlpack/tests/feedforward_network_test.cpp
@@ -104,35 +104,17 @@ void BuildVanillaNetwork(MatType& trainData,
 BOOST_AUTO_TEST_CASE(VanillaNetworkTest)
 {
   // Load the dataset.
-  arma::mat dataset;
-  data::Load("thyroid_train.csv", dataset, true);
+  arma::mat trainData;
+  data::Load("thyroid_train.csv", trainData, true);
 
-  arma::mat trainData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
+  arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
+  trainData.shed_row(trainData.n_rows - 1);
 
-  arma::mat trainLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-  arma::mat trainLabels = arma::zeros<arma::mat>(1, trainLabelsTemp.n_cols);
-  for (size_t i = 0; i < trainLabelsTemp.n_cols; ++i)
-  {
-    trainLabels(i) = arma::as_scalar(arma::find(
-        arma::max(trainLabelsTemp.col(i)) == trainLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testData;
+  data::Load("thyroid_test.csv", testData, true);
 
-  data::Load("thyroid_test.csv", dataset, true);
-
-  arma::mat testData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
-
-  arma::mat testLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-
-  arma::mat testLabels = arma::zeros<arma::mat>(1, testLabelsTemp.n_cols);
-  for (size_t i = 0; i < testLabels.n_cols; ++i)
-  {
-    testLabels(i) = arma::as_scalar(arma::find(
-        arma::max(testLabelsTemp.col(i)) == testLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testLabels = testData.row(testData.n_rows - 1);
+  testData.shed_row(testData.n_rows - 1);
 
   // Vanilla neural net with logistic activation function.
   // Because 92 percent of the patients are not hyperthyroid the neural
@@ -140,6 +122,7 @@ BOOST_AUTO_TEST_CASE(VanillaNetworkTest)
   BuildVanillaNetwork<>
       (trainData, trainLabels, testData, testLabels, 3, 8, 10, 0.1);
 
+  arma::mat dataset;
   dataset.load("mnist_first250_training_4s_and_9s.arm");
 
   // Normalize each point since these are images.
@@ -307,35 +290,17 @@ void BuildDropoutNetwork(MatType& trainData,
 BOOST_AUTO_TEST_CASE(DropoutNetworkTest)
 {
   // Load the dataset.
-  arma::mat dataset;
-  data::Load("thyroid_train.csv", dataset, true);
+  arma::mat trainData;
+  data::Load("thyroid_train.csv", trainData, true);
 
-  arma::mat trainData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
+  arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
+  trainData.shed_row(trainData.n_rows - 1);
 
-  arma::mat trainLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-  arma::mat trainLabels = arma::zeros<arma::mat>(1, trainLabelsTemp.n_cols);
-  for (size_t i = 0; i < trainLabelsTemp.n_cols; ++i)
-  {
-    trainLabels(i) = arma::as_scalar(arma::find(
-        arma::max(trainLabelsTemp.col(i)) == trainLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testData;
+  data::Load("thyroid_test.csv", testData, true);
 
-  data::Load("thyroid_test.csv", dataset, true);
-
-  arma::mat testData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
-
-  arma::mat testLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-
-  arma::mat testLabels = arma::zeros<arma::mat>(1, testLabelsTemp.n_cols);
-  for (size_t i = 0; i < testLabels.n_cols; ++i)
-  {
-    testLabels(i) = arma::as_scalar(arma::find(
-        arma::max(testLabelsTemp.col(i)) == testLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testLabels = testData.row(testData.n_rows - 1);
+  testData.shed_row(testData.n_rows - 1);
 
   // Vanilla neural net with logistic activation function.
   // Because 92 percent of the patients are not hyperthyroid the neural
@@ -343,6 +308,7 @@ BOOST_AUTO_TEST_CASE(DropoutNetworkTest)
   BuildDropoutNetwork<>
       (trainData, trainLabels, testData, testLabels, 3, 8, 10, 0.1);
 
+  arma::mat dataset;
   dataset.load("mnist_first250_training_4s_and_9s.arm");
 
   // Normalize each point since these are images.
@@ -436,35 +402,17 @@ void BuildDropConnectNetwork(MatType& trainData,
 BOOST_AUTO_TEST_CASE(DropConnectNetworkTest)
 {
   // Load the dataset.
-  arma::mat dataset;
-  data::Load("thyroid_train.csv", dataset, true);
+  arma::mat trainData;
+  data::Load("thyroid_train.csv", trainData, true);
 
-  arma::mat trainData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
+  arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
+  trainData.shed_row(trainData.n_rows - 1);
 
-  arma::mat trainLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-  arma::mat trainLabels = arma::zeros<arma::mat>(1, trainLabelsTemp.n_cols);
-  for (size_t i = 0; i < trainLabelsTemp.n_cols; ++i)
-  {
-    trainLabels(i) = arma::as_scalar(arma::find(
-        arma::max(trainLabelsTemp.col(i)) == trainLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testData;
+  data::Load("thyroid_test.csv", testData, true);
 
-  data::Load("thyroid_test.csv", dataset, true);
-
-  arma::mat testData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
-
-  arma::mat testLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-
-  arma::mat testLabels = arma::zeros<arma::mat>(1, testLabelsTemp.n_cols);
-  for (size_t i = 0; i < testLabels.n_cols; ++i)
-  {
-    testLabels(i) = arma::as_scalar(arma::find(
-        arma::max(testLabelsTemp.col(i)) == testLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testLabels = testData.row(testData.n_rows - 1);
+  testData.shed_row(testData.n_rows - 1);
 
   // Vanilla neural net with logistic activation function.
   // Because 92 percent of the patients are not hyperthyroid the neural
@@ -472,6 +420,7 @@ BOOST_AUTO_TEST_CASE(DropConnectNetworkTest)
   BuildDropConnectNetwork<>
       (trainData, trainLabels, testData, testLabels, 3, 8, 10, 0.1);
 
+  arma::mat dataset;
   dataset.load("mnist_first250_training_4s_and_9s.arm");
 
   // Normalize each point since these are images.
@@ -509,35 +458,17 @@ BOOST_AUTO_TEST_CASE(FFNMiscTest)
 BOOST_AUTO_TEST_CASE(SerializationTest)
 {
   // Load the dataset.
-  arma::mat dataset;
-  data::Load("thyroid_train.csv", dataset, true);
+  arma::mat trainData;
+  data::Load("thyroid_train.csv", trainData, true);
 
-  arma::mat trainData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
+  arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
+  trainData.shed_row(trainData.n_rows - 1);
 
-  arma::mat trainLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-  arma::mat trainLabels = arma::zeros<arma::mat>(1, trainLabelsTemp.n_cols);
-  for (size_t i = 0; i < trainLabelsTemp.n_cols; ++i)
-  {
-    trainLabels(i) = arma::as_scalar(arma::find(
-        arma::max(trainLabelsTemp.col(i)) == trainLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testData;
+  data::Load("thyroid_test.csv", testData, true);
 
-  data::Load("thyroid_test.csv", dataset, true);
-
-  arma::mat testData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
-
-  arma::mat testLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-
-  arma::mat testLabels = arma::zeros<arma::mat>(1, testLabelsTemp.n_cols);
-  for (size_t i = 0; i < testLabels.n_cols; ++i)
-  {
-    testLabels(i) = arma::as_scalar(arma::find(
-        arma::max(testLabelsTemp.col(i)) == testLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testLabels = testData.row(testData.n_rows - 1);
+  testData.shed_row(testData.n_rows - 1);
 
   // Vanilla neural net with logistic activation function.
   // Because 92 percent of the patients are not hyperthyroid the neural
@@ -576,35 +507,17 @@ BOOST_AUTO_TEST_CASE(SerializationTest)
 BOOST_AUTO_TEST_CASE(CustomLayerTest)
 {
   // Load the dataset.
-  arma::mat dataset;
-  data::Load("thyroid_train.csv", dataset, true);
+  arma::mat trainData;
+  data::Load("thyroid_train.csv", trainData, true);
 
-  arma::mat trainData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
+  arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
+  trainData.shed_row(trainData.n_rows - 1);
 
-  arma::mat trainLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-  arma::mat trainLabels = arma::zeros<arma::mat>(1, trainLabelsTemp.n_cols);
-  for (size_t i = 0; i < trainLabelsTemp.n_cols; ++i)
-  {
-    trainLabels(i) = arma::as_scalar(arma::find(
-        arma::max(trainLabelsTemp.col(i)) == trainLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testData;
+  data::Load("thyroid_test.csv", testData, true);
 
-  data::Load("thyroid_test.csv", dataset, true);
-
-  arma::mat testData = dataset.submat(0, 0, dataset.n_rows - 4,
-      dataset.n_cols - 1);
-
-  arma::mat testLabelsTemp = dataset.submat(dataset.n_rows - 3, 0,
-      dataset.n_rows - 1, dataset.n_cols - 1);
-
-  arma::mat testLabels = arma::zeros<arma::mat>(1, testLabelsTemp.n_cols);
-  for (size_t i = 0; i < testLabels.n_cols; ++i)
-  {
-    testLabels(i) = arma::as_scalar(arma::find(
-        arma::max(testLabelsTemp.col(i)) == testLabelsTemp.col(i), 1)) + 1;
-  }
+  arma::mat testLabels = testData.row(testData.n_rows - 1);
+  testData.shed_row(testData.n_rows - 1);
 
   FFN<NegativeLogLikelihood<>, RandomInitialization, CustomLayer<> > model;
   model.Add<Linear<> >(trainData.n_rows, 8);
diff --git a/src/mlpack/tests/kernel_test.cpp b/src/mlpack/tests/kernel_test.cpp
index cfe9361624..048e8eff6d 100644
--- a/src/mlpack/tests/kernel_test.cpp
+++ b/src/mlpack/tests/kernel_test.cpp
@@ -19,6 +19,7 @@
 #include <mlpack/core/kernels/polynomial_kernel.hpp>
 #include <mlpack/core/kernels/spherical_kernel.hpp>
 #include <mlpack/core/kernels/pspectrum_string_kernel.hpp>
+#include <mlpack/core/kernels/cauchy_kernel.hpp>
 #include <mlpack/core/metrics/lmetric.hpp>
 #include <mlpack/core/metrics/mahalanobis_distance.hpp>
 
@@ -608,4 +609,17 @@ BOOST_AUTO_TEST_CASE(PSpectrumStringEvaluateTest)
   BOOST_REQUIRE_CLOSE(p.Evaluate(b, a), 11.0, 1e-5);
 }
 
+/**
+ * Cauchy Kernel test.
+ */
+BOOST_AUTO_TEST_CASE(CauchyKernelTest)
+{
+  arma::vec a = "0 0 1";
+  arma::vec b = "0 1 0";
+
+  CauchyKernel ck(5.0);
+  BOOST_REQUIRE_CLOSE(ck.Evaluate(a, b), 0.92592588, 1e-5);
+  BOOST_REQUIRE_CLOSE(ck.Evaluate(b, a), 0.92592588, 1e-5);
+}
+
 BOOST_AUTO_TEST_SUITE_END();
diff --git a/src/mlpack/tests/kernel_traits_test.cpp b/src/mlpack/tests/kernel_traits_test.cpp
index 81e4f0f149..5a312e0413 100644
--- a/src/mlpack/tests/kernel_traits_test.cpp
+++ b/src/mlpack/tests/kernel_traits_test.cpp
@@ -52,6 +52,7 @@ BOOST_AUTO_TEST_CASE(IsNormalizedTest)
   BOOST_REQUIRE_EQUAL((bool) KernelTraits<SphericalKernel>::IsNormalized, true);
   BOOST_REQUIRE_EQUAL((bool) KernelTraits<TriangularKernel>::IsNormalized,
       true);
+  BOOST_REQUIRE_EQUAL((bool) KernelTraits<CauchyKernel>::IsNormalized, true);
 
   // Unnormalized kernels.
   BOOST_REQUIRE_EQUAL((bool) KernelTraits<LinearKernel>::IsNormalized, false);