updated docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404018

docs/docs/ml.xml

@@ -71,6 +71,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
          <item>empirical_kernel_map</item>
          <item>kkmeans</item>
          <item>svm_nu_trainer</item> 
+         <item>svm_one_class_trainer</item> 
          <item>svm_c_trainer</item> 
          <item>svm_c_linear_trainer</item> 
          <item>svm_c_ekm_trainer</item> 
@@ -944,6 +945,28 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
                                  
       </component>
       
+   <!-- ************************************************************************* -->
+
+      <component>
+         <name>svm_one_class_trainer</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/svm_one_class_trainer_abstract.h</spec_file>
+         <description>
+            <p>
+               Trains a one-class support vector classifier and outputs a <a href="#decision_function">decision_function</a>. 
+               It is implemented using the <a href="optimization.html#solve_qp3_using_smo">SMO</a> algorithm.
+            </p>
+               The implementation of the one-class training algorithm used by this library is based
+               on the following paper:
+               <ul>
+                  <li>Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector 
+                     machines, 2001. Software available at 
+                     <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">http://www.csie.ntu.edu.tw/~cjlin/libsvm</a></li>
+               </ul>
+         </description>
+                                 
+      </component>
+      
    <!-- ************************************************************************* -->
 
       <component>

docs/docs/optimization.xml

@@ -408,12 +408,14 @@ subject to the following constraint:
    This means that Q should be symmetric and positive-semidefinite.
 </pre>
 <br/>
-                This object implements the strategy used by the LIBSVM tool.  The following paper
+                This object implements the strategy used by the LIBSVM tool.  The following papers
                 can be consulted for additional details:
                <ul>
                   <li>Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector 
                      machines, 2001. Software available at 
                      <a href="http://www.csie.ntu.edu.tw/~cjlin/libsvm">http://www.csie.ntu.edu.tw/~cjlin/libsvm</a></li>
+                  <li>Working Set Selection Using Second Order Information for Training Support Vector Machines by
+                     Fan, Chen, and Lin.  In the Journal of Machine Learning Research 2005.</li>
                </ul>
 
          </description>

docs/docs/term_index.xml

@@ -166,6 +166,7 @@
          <term file="ml.html" name="is_binary_classification_problem"/>
          <term file="ml.html" name="svm_nu_trainer"/>
          <term file="ml.html" name="svm_c_trainer"/>
+         <term file="ml.html" name="svm_one_class_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"/>