Updated docs

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extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404116

docs/docs/ml.xml

@@ -83,6 +83,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
          <item>mlp</item> 
          <item>krls</item>
          <item>krr_trainer</item> 
+         <item>rr_trainer</item> 
          <item>svr_trainer</item> 
          <item>rvm_regression_trainer</item> 
          <item>rbf_network_trainer</item> 
@@ -952,9 +953,10 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
                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.
+            <a href="#linearly_independent_subset_finder">linearly_independent_subset_finder</a> to kernelize
+            the <a href="#rr_trainer">rr_trainer</a> object.  Thus it 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>
@@ -963,6 +965,27 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
                                  
       </component>
       
+   <!-- ************************************************************************* -->
+
+      <component>
+         <name>rr_trainer</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/rr_trainer_abstract.h</spec_file>
+         <description>
+            <p>
+               Performs linear ridge regression and outputs a <a href="#decision_function">decision_function</a> that 
+               represents the learned function.  In particular, this object can only be used with
+               the <a href="#linear_kernel">linear_kernel</a>.  It is optimized for the linear case where
+               the number of features in each sample vector is small (i.e. on the order of 1000 or less since the 
+               algorithm is cubic in the number of features.).  
+               If you want to use a nonlinear kernel then you should use the <a href="#krr_trainer">krr_trainer</a>.
+            </p>
+              This object is capable of automatically estimating its regularization parameter using 
+              leave-one-out cross-validation.  
+         </description>
+                                 
+      </component>
+      
    <!-- ************************************************************************* -->
 
       <component>

docs/docs/term_index.xml

@@ -180,6 +180,7 @@
          <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="rr_trainer"/>
          <term file="ml.html" name="svr_trainer"/>
          <term file="ml.html" name="rvm_regression_trainer"/>
          <term file="ml.html" name="rbf_network_trainer"/>
@@ -199,6 +200,7 @@
          <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#rr_trainer" name="ridge regression"/>
          <term link="ml.html#svr_trainer" name="support vector regression"/>
          <term link="ml.html#krr_trainer" name="regularized least squares"/>
          <term link="ml.html#krr_trainer" name="least squares SVM"/>