updated docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%404203

docs/docs/index.xml

@@ -130,6 +130,9 @@
                   <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>General purpose <a href="ml.html#one_vs_one_trainer">multiclass classification</a> tools</li>
+                  <li>A <a href="ml.html#svm_multiclass_linear_trainer">Multiclass SVM</a></li>
+                  <li>A tool for solving the optimization problem associated with 
+                     <a href="optimization.html#structural_svm_problem">structural support vector machines</a>. </li> 
                   <li>An online <a href="ml.html#krls">kernel RLS regression</a> algorithm</li>
                   <li>An online <a href="ml.html#svm_pegasos">SVM classification</a> algorithm</li>
                   <li>An online kernelized <a href="ml.html#kcentroid">centroid estimator</a>/novelty detector</li> and

docs/docs/optimization.xml

@@ -680,7 +680,7 @@ subject to the following constraint:
                 A structural SVM, on the other hand, can learn to predict outputs as complex
                 as entire parse trees.  To do this, it learns a function F(x,y) which measures 
                 how well a particular data sample x matches a label y.  When used for prediction,
-                the best label for a new x is then given by the y which maximizes F(x,y).   
+                the best label for a new x is given by the y which maximizes F(x,y).   
 
                 <br/>
                 <br/>

docs/docs/release_notes.xml

@@ -12,12 +12,36 @@
 
 <current>
 New Stuff:
+   - Added a multiclass support vector machine.
+   - Added a tool for solving the optimization problem associated with
+     structural support vector machines.
+   - Added new functions for dealing with sparse vectors: add_to(), 
+     subtract_from(), max_index_plus_one(), fix_nonzero_indexing(), a
+     more flexible dot(), and I renamed assign_dense_to_sparse() to assign() 
+     and made it more flexible.
  
 Non-Backwards Compatible Changes:
+   - Renamed max_index_value_plus_one() (a function for working with graphs) to 
+     max_index_plus_one() so that it uses the same name as the essentially 
+     identical function for working with sparse vectors.
+   - I simplified the cross_validate_multiclass_trainer(), cross_validate_trainer(), 
+     test_binary_decision_function(), and test_multiclass_decision_function()
+     routines.  They now always return double matrices regardless of any other 
+     consideration.  This only breaks previous code if you had been assigning
+     the result into a float or long double matrix.
+   - Renamed assign_dense_to_sparse() to assign()
 
 Bug fixes:
+   - Fixed a bug in load_libsvm_formatted_data().  I had forgotten to clear the 
+     contents of the labels output vector before adding the loaded label data.  
+   - Fixed a bug in the kernel_matrix() function.  It didn't compile when used 
+     with sparse samples which were of type std::vector&lt;std::pair&lt;&gt; &gt;.  
+     Moreover, some of the trainers have a dependency on kernel_matrix() so this 
+     fix makes those trainers also work with this kind of sparse sample.
 
 Other:
+   - Added a value_type typedef to matrix_exp so it's easier to write templates
+     which operate on STL containers and matrix objects.
 </current>
 
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docs/docs/term_index.xml

@@ -133,6 +133,7 @@
          <term file="dlib/svm/multiclass_tools_abstract.h.html#find_missing_pairs" name="find_missing_pairs"/>
 
          <term file="ml.html" name="svm_multiclass_linear_trainer"/>
+         <term link="ml.html#svm_multiclass_linear_trainer" name="Multiclass SVM"/>
          <term file="ml.html" name="one_vs_one_trainer"/>
          <term file="ml.html" name="one_vs_one_decision_function"/>
          <term file="ml.html" name="multiclass_linear_decision_function"/>