updated docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403574

docs/docs/ml.xml

@@ -71,6 +71,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
          <item>kkmeans</item>
          <item>svm_nu_trainer</item> 
          <item>svm_c_linear_trainer</item> 
+         <item>svm_c_ekm_trainer</item> 
          <item>rvm_trainer</item> 
          <item>rvm_regression_trainer</item> 
          <item>rbf_network_trainer</item> 
@@ -651,6 +652,26 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
                                  
       </component>
       
+   <!-- ************************************************************************* -->
+
+      <component>
+         <name>svm_c_ekm_trainer</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/svm_c_ekm_trainer_abstract.h</spec_file>
+         <description>
+                This object represents a tool for training the C formulation of 
+                a support vector machine.   It is implemented using the <a href="#empirical_kernel_map">empirical_kernel_map</a>
+                to kernelize the <a href="#svm_c_linear_trainer">svm_c_linear_trainer</a>.  This makes it a very fast algorithm
+                but means the user must supply a set of basis vectors.  
+
+               <p>
+                For details about the "basis vectors" see the empirical_kernel_map 
+                documentation.  In particular, see it's <a href="empirical_kernel_map_ex.cpp.html">example program</a>.
+               </p>
+         </description>
+                                 
+      </component>
+      
    <!-- ************************************************************************* -->
 
       <component>

docs/docs/release_notes.xml

@@ -12,6 +12,7 @@
 
 <current>
 New Stuff:
+   - Added the svm_c_ekm_trainer 
 
 Non-Backwards Compatible Changes:
 

docs/docs/term_index.xml

@@ -133,6 +133,7 @@
          <term file="ml.html" name="is_binary_classification_problem"/>
          <term file="ml.html" name="svm_nu_trainer"/>
          <term file="ml.html" name="svm_c_linear_trainer"/>
+         <term file="ml.html" name="svm_c_ekm_trainer"/>
          <term file="ml.html" name="rvm_trainer"/>
          <term file="ml.html" name="rvm_regression_trainer"/>
          <term file="ml.html" name="rbf_network_trainer"/>