updated the docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402460

docs/docs/algorithms.xml

@@ -966,6 +966,54 @@
          </description>
       </component>
       
+   <!-- ************************************************************************* -->
+
+      <component>
+         <name>rvm_regression_trainer</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/rvm_abstract.h</spec_file>
+         <description>
+            <p>
+               Trains a relevance vector machine for solving regression problems.  
+               Outputs a <a href="#decision_function">decision_function</a> that represents the learned 
+               regression function. 
+            </p>
+               The implementation of the RVM training algorithm used by this library is based
+               on the following paper:
+               <blockquote>
+                Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation 
+                for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings 
+                of the Ninth International Workshop on Artificial Intelligence and Statistics, 
+                Key West, FL, Jan 3-6.
+               </blockquote>
+         </description>
+                                 
+      </component>
+      
+   <!-- ************************************************************************* -->
+
+      
+      <component>
+         <name>rvm_trainer</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/rvm_abstract.h</spec_file>
+         <description>
+            <p>
+               Trains a relevance vector machine for solving binary classification problems.  
+               Outputs a <a href="#decision_function">decision_function</a> that represents the learned classifier. 
+            </p>
+               The implementation of the RVM training algorithm used by this library is based
+               on the following paper:
+               <blockquote>
+                Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood maximisation 
+                for sparse Bayesian models. In C. M. Bishop and B. J. Frey (Eds.), Proceedings 
+                of the Ninth International Workshop on Artificial Intelligence and Statistics, 
+                Key West, FL, Jan 3-6.
+               </blockquote>
+         </description>
+                                 
+      </component>
+      
    <!-- ************************************************************************* -->
 
       

docs/docs/index.xml

@@ -115,15 +115,17 @@
 
             <li><b>Machine Learning Algorithms</b>
                <ul>
-                  <li><a href="algorithms.html#mlp">multi layer perceptrons</a> </li>  
-                  <li><a href="algorithms.html#svm_nu_trainer">support vector machines</a> for classification</li>
+                  <li><a href="algorithms.html#mlp">Multi layer perceptrons</a> </li>  
+                  <li><a href="algorithms.html#svm_nu_trainer">Support vector machines</a> for classification</li>
+                  <li>Relevance vector machines for <a href="algorithms.html#rvm_trainer">classification</a> 
+                      and <a href="algorithms.html#rvm_regression_trainer">regression</a> </li>
                   <li>An online <a href="algorithms.html#krls">kernel RLS regression</a> algorithm</li>
                   <li>An online kernelized <a href="algorithms.html#kcentroid">centroid estimator</a>/novelty detector</li>
                   <li>A kernelized <a href="algorithms.html#kkmeans">k-means</a> clustering algorithm</li>
                   <li><a href="algorithms.html#rbf_network_trainer">Radial Basis Function Networks</a></li>
-                  <li>Bayesian Network inference algorithms such as the 
+                  <li>Bayesian network inference algorithms such as the 
                      <a href="algorithms.html#bayesian_network_join_tree">join tree</a> algorithm and 
-                     <a href="algorithms.html#bayesian_network_gibbs_sampler">Gibbs sampler</a> Markov Chain Monte Carlo algorithm</li>
+                     <a href="algorithms.html#bayesian_network_gibbs_sampler">gibbs sampler</a> markov chain monte carlo algorithm</li>
                </ul>
             </li>
 

docs/docs/term_index.xml

@@ -392,6 +392,8 @@
          <term link="algorithms.html#is_binary_classification_problem" name="is_binary_classification_problem"/>
          <term link="algorithms.html#square_root" name="square_root"/>
          <term link="algorithms.html#svm_nu_trainer" name="svm_nu_trainer"/>
+         <term link="algorithms.html#rvm_trainer" name="rvm_trainer"/>
+         <term link="algorithms.html#rvm_regression_trainer" name="rvm_regression_trainer"/>
          <term link="algorithms.html#rbf_network_trainer" name="rbf_network_trainer"/>
          <term link="algorithms.html#reduced" name="reduced"/>
          <term link="algorithms.html#reduced_decision_function_trainer" name="reduced_decision_function_trainer"/>
@@ -402,6 +404,7 @@
          <term link="algorithms.html#cross_validate_trainer_threaded" name="cross_validate_trainer_threaded"/>
          <term link="algorithms.html#test_binary_decision_function" name="test_binary_decision_function"/>
          <term link="algorithms.html#svm_nu_trainer" name="support vector machine"/>
+         <term link="algorithms.html#rvm_trainer" name="relevance vector machine"/>
          <term link="algorithms.html#vector" name="vector"/>
          <term link="algorithms.html#point" name="point"/>
          <term link="algorithms.html#krls" name="krls"/>