Added more meat to a comment

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402515

examples/kkmeans_ex.cpp

@@ -37,12 +37,28 @@ int main()
     // results without much fiddling.
     typedef radial_basis_kernel<sample_type> kernel_type;
 
+
     // Here we declare an instance of the kcentroid object.  The first argument to the constructor
     // is the kernel we wish to use.  The second is a parameter that determines the numerical 
     // accuracy with which the object will perform part of the learning algorithm.  Generally
     // smaller values give better results but cause the algorithm to run slower.  You just have
     // to play with it to decide what balance of speed and accuracy is right for your problem.
     // Here we have set it to 0.01.
+    //
+    // Also, since we are using the radial basis kernel we have to pick the RBF width parameter.
+    // Here we have it set to 0.1.  But in general, a reasonable way of picking this value is
+    // to start with some initial guess and to just run all the data through the resulting 
+    // kcentroid.  Then print out kc.dictionary_size() to see how many support vectors the 
+    // kcentroid object is using.  A good rule of thumb is that you should have somewhere 
+    // in the range of 10-100 support vectors (but this rule isn't carved in stone).  
+    // So if you aren't in that range then you can change the RBF parameter.  Making it 
+    // smaller will decrease the dictionary size and making it bigger will increase the 
+    // dictionary size.   
+    //
+    // So what I often do is I set the kcentroid's second parameter to 0.01 or 0.001.  Then
+    // I find an RBF kernel parameter that gives me the number of support vectors that I 
+    // feel is appropriate for the problem I'm trying to solve.  Again, this just comes down
+    // to playing with it and getting a feel for how things work.
     kcentroid<kernel_type> kc(kernel_type(0.1),0.01);
 
     // Now we make an instance of the kkmeans object and tell it to use kcentroid objects