Make the list of classes first, before the list of applications.

doc/user/core/distances.md

@@ -2,7 +2,20 @@
 
 mlpack includes a number of distance metrics for its distance-based techniques.
 These all implement the [same API](../../developer/distances.md), providing one
-`Evaluate()` method, and can be used with a variety of different techniques,
+`Evaluate()` method.  mlpack provides a number of supported distance metrics:
+
+ * [`LMetric`](#lmetric): generalized L-metric/Lp-metric, including
+   Manhattan/Euclidean/Chebyshev distances
+ * [`IoUDistance`](#ioudistance): intersection-over-union distance
+ * [`IPMetric<KernelType>`](#ipmetrickerneltype): inner product metric (e.g.
+   induced metric over a [Mercer kernel](kernels.md))
+ * [`MahalanobisDistance`](#mahalanobisdistance): weighted Euclidean distance
+   with weights specified by a covariance matrix
+ * [Implement a custom metric](../../developer/distances.md)
+
+---
+
+These distance metrics can be used with a variety of different techniques,
 including:
 
 <!-- TODO: better names for each link -->
@@ -15,17 +28,6 @@ including:
  * [`RANN`](/src/mlpack/methods/rann/rann.hpp)
  * [`KMeans`](/src/mlpack/methods/kmeans/kmeans.hpp)
 
-Supported metrics:
-
- * [`LMetric`](#lmetric): generalized L-metric/Lp-metric, including
-   Manhattan/Euclidean/Chebyshev distances
- * [`IoUDistance`](#ioudistance): intersection-over-union distance
- * [`IPMetric<KernelType>`](#ipmetrickerneltype): inner product metric (e.g.
-   induced metric over a [Mercer kernel](kernels.md))
- * [`MahalanobisDistance`](#mahalanobisdistance): weighted Euclidean distance
-   with weights specified by a covariance matrix
- * [Implement a custom metric](../../developer/distances.md)
-
 ## `LMetric`
 
 The `LMetric` template class implements a [generalized

doc/user/core/kernels.md

@@ -2,18 +2,7 @@
 
 mlpack includes a number of Mercer kernels for its kernel-based techniques.
 These all implement the [same API](../../developer/kernels.md), providing one
-`Evaluate()` method, and can be used with a variety of different techniques,
-including:
-
-<!-- TODO: document everything below -->
-
- * [`KDE`](/src/mlpack/methods/kde/kde.hpp)
- * [`MeanShift`](/src/mlpack/methods/mean_shift/mean_shift.hpp)
- * [`KernelPCA`](/src/mlpack/methods/kernel_pca/kernel_pca.hpp)
- * [`FastMKS`](/src/mlpack/methods/fastmks/fastmks.hpp)
- * [`NystroemMethod`](/src/mlpack/methods/nystroem_method/nystroem_method.hpp)
-
-Supported kernels:
+`Evaluate()` method.  mlpack provides a number of supported kernels:
 
  * [`GaussianKernel`](#gaussiankernel): standard Gaussian/radial basis
    function/RBF kernel
@@ -37,6 +26,17 @@ Supported kernels:
 
 ---
 
+These kernels can then be used in a number of machine learning algorithms that
+mlpack provides:
+
+<!-- TODO: document everything below -->
+
+ * [`KDE`](/src/mlpack/methods/kde/kde.hpp)
+ * [`MeanShift`](/src/mlpack/methods/mean_shift/mean_shift.hpp)
+ * [`KernelPCA`](/src/mlpack/methods/kernel_pca/kernel_pca.hpp)
+ * [`FastMKS`](/src/mlpack/methods/fastmks/fastmks.hpp)
+ * [`NystroemMethod`](/src/mlpack/methods/nystroem_method/nystroem_method.hpp)
+
 ## `GaussianKernel`
 
 The `GaussianKernel` class implements the standard [Gaussian