updated the docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402415

docs/docs/algorithms.xml

@@ -28,7 +28,6 @@
          <item>crc32</item> 
          <item>rand</item> 
          <item>running_stats</item> 
-         <item>pick_initial_centers</item> 
          <item>mlp</item> 
          <item nolink="true">
             <name>Optimization</name>
@@ -105,16 +104,28 @@
             </sub>
          </item>
          <item nolink="true">
-            <name>SVM Utilities</name>
+            <name>Kernel_Methods</name>
             <sub>
-               <item>radial_basis_kernel</item>
-               <item>polynomial_kernel</item>
-               <item>linear_kernel</item>
-               <item>decision_function</item>
-               <item>distance_function</item>
-               <item>probabilistic_decision_function</item>
+               <item nolink="true">
+                  <name>Kernels</name>
+                  <sub>
+                     <item>radial_basis_kernel</item>
+                     <item>polynomial_kernel</item>
+                     <item>linear_kernel</item>
+                  </sub>
+               </item>
+               <item nolink="true">
+                  <name>Function_Objects</name>
+                  <sub>
+                     <item>decision_function</item>
+                     <item>distance_function</item>
+                     <item>probabilistic_decision_function</item>
+                  </sub>
+               </item>
+               <item>pick_initial_centers</item> 
                <item>krls</item>
                <item>kcentroid</item>
+               <item>linearly_independent_subset_finder</item>
                <item>kkmeans</item>
                <item>svm_nu_trainer</item> 
                <item>rbf_network_trainer</item> 
@@ -856,6 +867,39 @@
       
       
    <!-- ************************************************************************* -->
+
+      <component>
+         <name>linearly_independent_subset_finder</name>
+         <file>dlib/svm.h</file>
+         <spec_file link="true">dlib/svm/linearly_independent_subset_finder_abstract.h</spec_file>
+         <description>
+            <p>
+                This is an implementation of an online algorithm for recursively finding a
+                set of linearly independent vectors in a kernel induced feature space.  To 
+                use it you decide how large you would like the set to be and then you feed it 
+                sample points.  
+            </p>
+            <p>
+                
+                Each time you present it with a new sample point it either 
+                keeps the current set of independent points unchanged, or if the new point 
+                is "more linearly independent" than one of the points it already has,  
+                it replaces the weakly linearly independent point with the new one.
+            </p>
+
+            <p>
+                
+                This object uses the Approximately Linearly Dependent metric described in the paper 
+                The Kernel Recursive Least Squares Algorithm by Yaakov Engel to decide which
+                points are more linearly independent than others.
+            </p>
+         </description>
+
+      </component>
+      
+      
+   <!-- ************************************************************************* -->
+      
       
       <component>
          <name>kcentroid</name>

docs/docs/term_index.xml

@@ -402,6 +402,7 @@
          <term link="algorithms.html#point" name="point"/>
          <term link="algorithms.html#krls" name="krls"/>
          <term link="algorithms.html#kcentroid" name="kcentroid"/>
+         <term link="algorithms.html#linearly_independent_subset_finder" name="linearly_independent_subset_finder"/>
          <term link="algorithms.html#running_stats" name="running_stats"/>
          <term link="algorithms.html#kkmeans" name="kkmeans"/>
          <term link="algorithms.html#pick_initial_centers" name="pick_initial_centers"/>