updated docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403765

docs/docs/index.xml

@@ -119,6 +119,8 @@
                <ul>
                   <li><a href="ml.html#mlp">Multi layer perceptrons</a> </li>  
                   <li><a href="ml.html#svm_nu_trainer">Support vector machines</a> for classification</li>
+                  <li>Reduced-rank methods for large-scale <a href="ml.html#svm_c_ekm_trainer">classification</a>
+                     and <a href="ml.html#krr_trainer">regression</a></li>
                   <li>Relevance vector machines for <a href="ml.html#rvm_trainer">classification</a> 
                       and <a href="ml.html#rvm_regression_trainer">regression</a> </li>
                   <li>An online <a href="ml.html#krls">kernel RLS regression</a> algorithm</li>

docs/docs/main_menu.xml

@@ -175,6 +175,14 @@
                   <name>Relevance_Vector_Classification</name>
                   <link>rvm_ex.cpp.html</link>
                </item>
+               <item>
+                  <name>Kernel_Ridge_Regression</name>
+                  <link>krr_regression_ex.cpp.html</link>
+               </item>
+               <item>
+                  <name>KRR_Classification</name>
+                  <link>krr_classification_ex.cpp.html</link>
+               </item>
                <item>
                   <name>Support_Vector_Machine</name>
                   <link>svm_ex.cpp.html</link>

docs/docs/ml.xml

@@ -74,6 +74,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
          <item>svm_c_linear_trainer</item> 
          <item>svm_c_ekm_trainer</item> 
          <item>rvm_trainer</item> 
+         <item>krr_trainer</item> 
          <item>rvm_regression_trainer</item> 
          <item>rbf_network_trainer</item> 
          <item>rank_features</item> 
@@ -867,6 +868,29 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
                                  
       </component>
       
+   <!-- ************************************************************************* -->
+
+      <component>
+         <name>krr_trainer</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/krr_trainer_abstract.h</spec_file>
+         <description>
+            <p>
+               Performs kernel ridge regression and outputs a <a href="#decision_function">decision_function</a> that 
+               represents the learned function. 
+            </p>
+            The implementation is done using the <a href="#empirical_kernel_map">empirical_kernel_map</a> and 
+            <a href="#linearly_independent_subset_finder">linearly_independent_subset_finder</a> 
+            and thus allows you to run the algorithm on large datasets and obtain sparse outputs.  It is also
+            capable of automatically estimating its regularization parameter using leave-one-out cross-validation.
+         </description>
+         <examples>
+            <example>krr_regression_ex.cpp.html</example>
+            <example>krr_classification_ex.cpp.html</example>
+         </examples>
+                                 
+      </component>
+      
    <!-- ************************************************************************* -->
 
       

docs/docs/term_index.xml

@@ -155,6 +155,7 @@
          <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="krr_trainer"/>
          <term file="ml.html" name="rvm_regression_trainer"/>
          <term file="ml.html" name="rbf_network_trainer"/>
          <term file="ml.html" name="reduced"/>
@@ -167,6 +168,9 @@
          <term file="ml.html" name="test_binary_decision_function"/>
          <term link="ml.html#svm_nu_trainer" name="support vector machine"/>
          <term link="ml.html#rvm_trainer" name="relevance vector machine"/>
+         <term link="ml.html#krr_trainer" name="kernel ridge regression"/>
+         <term link="ml.html#krr_trainer" name="regularized least squares"/>
+         <term link="ml.html#krr_trainer" name="least squares SVM"/>
          <term file="ml.html" name="krls"/>
          <term file="ml.html" name="kcentroid"/>
          <term file="ml.html" name="linearly_independent_subset_finder"/>