updated docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404007

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_c_trainer</item> 
          <item>svm_c_linear_trainer</item> 
          <item>svm_c_ekm_trainer</item> 
          <item>rvm_trainer</item> 
@@ -918,7 +919,6 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
       
    <!-- ************************************************************************* -->
 
-      
       <component>
          <name>svm_nu_trainer</name>
          <file>dlib/svm.h</file>
@@ -926,6 +926,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
          <description>
             <p>
                Trains a nu support vector classifier and outputs a <a href="#decision_function">decision_function</a>. 
+               It is implemented using the <a href="optimization.html#solve_qp2_using_smo">SMO</a> algorithm.
             </p>
                The implementation of the nu-svm training algorithm used by this library is based
                on the following excellent papers:
@@ -943,6 +944,28 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
                                  
       </component>
       
+   <!-- ************************************************************************* -->
+
+      <component>
+         <name>svm_c_trainer</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/svm_c_trainer_abstract.h</spec_file>
+         <description>
+            <p>
+               Trains a C 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 C-SVM 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

@@ -32,6 +32,7 @@
          <item>find_min_bobyqa</item> 
          <item>solve_qp_using_smo</item> 
          <item>solve_qp2_using_smo</item> 
+         <item>solve_qp3_using_smo</item> 
          <item>oca</item> 
          <item>find_max</item> 
          <item>find_max_single_variable</item> 
@@ -388,6 +389,37 @@ subject to the following constraint:
                                  
       </component>
 
+   <!-- ************************************************************************* -->
+      
+      <component>
+         <name>solve_qp3_using_smo</name>
+         <file>dlib/optimization.h</file>
+         <spec_file link="true">dlib/optimization/optimization_solve_qp3_using_smo_abstract.h</spec_file>
+         <description>
+             This function solves the following quadratic program:
+<pre>
+   Minimize: f(alpha) == 0.5*trans(alpha)*Q*alpha + trans(p)*alpha
+   subject to the following constraints:
+        for all i such that y(i) == +1:  0 &lt;= alpha(i) &lt;= Cp 
+        for all i such that y(i) == -1:  0 &lt;= alpha(i) &lt;= Cn 
+        trans(y)*alpha == B 
+
+   Where all elements of y must be equal to +1 or -1 and f is convex.  
+   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
+                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>
+               </ul>
+
+         </description>
+                                 
+      </component>
+
    <!-- ************************************************************************* -->
       
       <component>

docs/docs/term_index.xml

@@ -52,6 +52,7 @@
          <term file="optimization.html" name="find_min_bobyqa"/>
          <term file="optimization.html" name="solve_qp_using_smo"/>
          <term file="optimization.html" name="solve_qp2_using_smo"/>
+         <term file="optimization.html" name="solve_qp3_using_smo"/>
          <term file="optimization.html" name="oca"/>
          <term link="optimization.html#find_min_bobyqa" name="BOBYQA"/>
          <term file="optimization.html" name="find_max"/>
@@ -164,6 +165,7 @@
          <term file="ml.html" name="simplify_linear_decision_function"/>
          <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_c_linear_trainer"/>
          <term file="ml.html" name="svm_c_ekm_trainer"/>
          <term file="ml.html" name="rvm_trainer"/>