diff --git a/doc/tutorials/approx_kfn.md b/doc/tutorials/approx_kfn.md
index 01613b11a0..28d71dc28d 100644
--- a/doc/tutorials/approx_kfn.md
+++ b/doc/tutorials/approx_kfn.md
@@ -812,7 +812,7 @@ extern arma::mat dataset;
 QDAFN<> qdafn(dataset, 10, 5);
 
 // Print the fifth point of the candidate set.
-std::cout << ds.CandidateSet(2).col(4).t();
+std::cout << qdafn.CandidateSet(2).col(4).t();
 ```
 
 ### Retraining on a new reference set
@@ -896,7 +896,7 @@ extern arma::mat querySet;
 
 // Construct the object, performing the default dual-tree search with
 // approximation level epsilon = 0.05.
-KFN kfn(dataset, KFN::DUAL_TREE_MODE, 0.05);
+KFN kfn(dataset, DUAL_TREE_MODE, 0.05);
 
 // Search for approximate furthest neighbors.
 arma::Mat<size_t> neighbors;
diff --git a/doc/tutorials/fastmks.md b/doc/tutorials/fastmks.md
index e25fe766fd..21a0d4afb8 100644
--- a/doc/tutorials/fastmks.md
+++ b/doc/tutorials/fastmks.md
@@ -257,6 +257,9 @@ manually specified.  Choices that mlpack provides include:
  - `HyperbolicTangentKernel`
  - `LaplacianKernel`
  - `PSpectrumStringKernel`
+ - `CauchyKernal`
+ - `ExampleKernal`
+ - `SphericalKernal`
 
 The following examples use kernels from that list.  Writing your own kernel is
 detailed in the next section.  Remember that when you are using the C++
@@ -293,7 +296,7 @@ f.Search(5, indices, products);
 In this setting we have both a query and reference dataset.  We search for 10
 maximum kernels.
 
-```
+```c++
 #include <mlpack.hpp>
 
 using namespace mlpack::fastmks;