Added missing comments and fixed some existing ones.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402230

dlib/svm/krls_abstract.h

@@ -7,6 +7,7 @@
 #include "../matrix/matrix_abstract.h"
 #include "../algs.h"
 #include "../serialize.h"
+#include "kernel_abstract.h"
 
 namespace dlib
 {
@@ -17,6 +18,9 @@ namespace dlib
     class krls
     {
         /*!
+            REQUIREMENTS ON kernel_type
+                is a kernel function object as defined in dlib/svm/kernel_abstract.h 
+
             INITIAL VALUE
                 - dictionary_size() == 0
 

dlib/svm/svm_abstract.h

@@ -62,8 +62,8 @@ namespace dlib
                 - y(i) == -1 or +1
                 - y(i) is the class that should be assigned to training example x(i)
             - 0 < nu < maximum_nu(y) 
-            - kernel_function == a kernel function object type as defined at the top 
-              of this document.
+            - kernel_function == a kernel function object type as defined at the
+              top of dlib/svm/kernel_abstract.h
         ensures
             - trains a nu support vector classifier given the training samples in x and 
               labels in y.  Training is done when the error is less than eps.
@@ -112,8 +112,8 @@ namespace dlib
                 - y(i) == -1 or +1
                 - y(i) is the class that should be assigned to training example x(i)
             - 0 < nu < maximum_nu(y) 
-            - kernel_function == a kernel function object type as defined at the top 
-              of this document.
+            - kernel_function == a kernel function object type as defined at the
+              top of dlib/svm/kernel_abstract.h
         ensures
             - trains a nu support vector classifier given the training samples in x and 
               labels in y.  Training is done when the error is less than eps.
@@ -158,8 +158,8 @@ namespace dlib
                 - y(i) == -1 or +1
                 - y(i) is the class that should be assigned to training example x(i)
             - 0 < nu < maximum_nu(y) 
-            - kernel_function == a kernel function object type as defined at the top 
-              of this document.
+            - kernel_function == a kernel function object type as defined at the
+              top of dlib/svm/kernel_abstract.h
         ensures
             - performs k-fold cross validation by training a nu-svm using the svm_nu_train()
               function.  Each fold is tested using the learned decision_function and the