updated docs

docs/docs/ml.xml

@@ -90,6 +90,7 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
          <item>krr_trainer</item> 
          <item>rr_trainer</item> 
          <item>svr_trainer</item> 
+         <item>svr_linear_trainer</item> 
          <item>rvm_regression_trainer</item> 
          <item>rbf_network_trainer</item> 
       </section>
@@ -1081,7 +1082,11 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
          <description>
             <p>
                 This object implements a trainer for performing epsilon-insensitive support 
-                vector regression.  It is implemented using the <a href="optimization.html#solve_qp3_using_smo">SMO</a> algorithm.
+                vector regression.  It is implemented using the <a href="optimization.html#solve_qp3_using_smo">SMO</a> algorithm, 
+                allowing the use of non-linear kernels.
+                If you are interested in performing support vector regression with a linear kernel and you
+                have a lot of training data then you should use the <a href="#svr_linear_trainer">svr_linear_trainer</a>
+                which is highly optimized for this case.
             </p>
                 The implementation of the eps-SVR training algorithm used by this object is based
                 on the following paper:
@@ -1097,6 +1102,21 @@ Davis E. King. <a href="http://jmlr.csail.mit.edu/papers/volume10/king09a/king09
                                  
       </component>
       
+   <!-- ************************************************************************* -->
+
+      <component>
+         <name>svr_linear_trainer</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/svr_linear_trainer_abstract.h</spec_file>
+         <description>
+                This object implements a trainer for performing epsilon-insensitive support
+                vector regression.  It uses the <a href="optimization.html#oca">oca</a> 
+                optimizer so it is very efficient at solving this problem when
+                linear kernels are used, making it suitable for use with large
+                datasets. 
+         </description>
+      </component>
+      
    <!-- ************************************************************************* -->
 
       <component>

docs/docs/term_index.xml

@@ -316,6 +316,7 @@
          <term file="ml.html" name="krr_trainer"                                    include="dlib/svm.h"/>
          <term file="ml.html" name="rr_trainer"                                     include="dlib/svm.h"/>
          <term file="ml.html" name="svr_trainer"                                    include="dlib/svm.h"/>
+         <term file="ml.html" name="svr_linear_trainer"                             include="dlib/svm.h"/>
          <term file="ml.html" name="rvm_regression_trainer"                         include="dlib/svm.h"/>
          <term file="ml.html" name="rbf_network_trainer"                            include="dlib/svm.h"/>
          <term file="ml.html" name="reduced"                                        include="dlib/svm.h"/>