updated docs

docs/docs/index.xml

@@ -141,7 +141,7 @@
                   <li>A kernelized <a href="ml.html#kkmeans">k-means</a> clustering algorithm</li>
                   <li><a href="ml.html#rbf_network_trainer">Radial Basis Function Networks</a></li>
                   <li><a href="ml.html#mlp">Multi layer perceptrons</a> </li>  
-                  <li><a href="ml.html#structural_sequence_labeling_trainer">Sequence labeling</a> </li>  
+                  <li>Tools for <a href="ml.html#sequence_labeler">sequence labeling</a> </li>  
                </ul>
             </li>
 

docs/docs/ml.xml

@@ -1309,8 +1309,17 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
          <file>dlib/svm.h</file>
          <spec_file link="true">dlib/svm/sequence_labeler_abstract.h</spec_file>
          <description>
-            This object is a tool for labeling each element of a sequence with
-            a discrete label.
+               This object is a tool for doing sequence labeling.  In particular,
+               it is capable of representing sequence labeling models such as
+               those produced by Hidden Markov SVMs or Conditional Random fields.
+               See the following papers for an introduction to these techniques:
+               <blockquote>
+                    Hidden Markov Support Vector Machines by 
+                     Y. Altun, I. Tsochantaridis, T. Hofmann
+                  <br/>
+                    Shallow Parsing with Conditional Random Fields by 
+                     Fei Sha and Fernando Pereira
+               </blockquote>
          </description>
                                  
       </component>
@@ -2240,13 +2249,21 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
          <file>dlib/svm_threaded.h</file>
          <spec_file link="true">dlib/svm/structural_svm_sequence_labeling_problem_abstract.h</spec_file>
          <description>
-               This object is a tool for learning the parameter vector needed to use
+                This object is a tool for learning the weight vector needed to use
                 a <a href="#sequence_labeler">sequence_labeler</a> object.  
 
-            <p>
-               It learns the parameter vector by formulating the problem as a <a href="#structural_svm_problem">structural 
-               SVM problem</a>.  
-            </p>
+                It learns the parameter vector by formulating the problem as a 
+                <a href="#structural_svm_problem">structural SVM problem</a>.  
+                The general approach is discussed in the paper:
+                <blockquote>
+                    Hidden Markov Support Vector Machines by 
+                    Y. Altun, I. Tsochantaridis, T. Hofmann
+                </blockquote>
+                While the particular optimization strategy used is the method from: 
+                <blockquote>
+                    T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of 
+                    Structural SVMs, Machine Learning, 77(1):27-59, 2009.
+                </blockquote>
          </description>
                                  
       </component>
@@ -2281,7 +2298,10 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
          <file>dlib/svm_threaded.h</file>
          <spec_file link="true">dlib/svm/structural_sequence_labeling_trainer_abstract.h</spec_file>
          <description>
-               This object is a tool for learning to do sequence labeling. 
+                This object is a tool for learning to do sequence labeling based
+                on a set of training data.  The training procedure produces a
+                <a href="#sequence_labeler">sequence_labeler</a> object which can 
+                be use to predict the labels of new data sequences.
                <p>
                Note that this is just a convenience wrapper around the 
                <a href="#structural_svm_sequence_labeling_problem">structural_svm_sequence_labeling_problem</a>