updated docs

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%403782

docs/docs/index.xml

@@ -117,8 +117,7 @@
 
             <li><b>Machine Learning Algorithms</b>
                <ul>
-                  <li><a href="ml.html#mlp">Multi layer perceptrons</a> </li>  
-                  <li><a href="ml.html#svm_nu_trainer">Support vector machines</a> for classification</li>
+                  <li>Conventional SMO based <a href="ml.html#svm_nu_trainer">Support vector machines</a> for classification</li>
                   <li>Reduced-rank methods for large-scale <a href="ml.html#svm_c_ekm_trainer">classification</a>
                      and <a href="ml.html#krr_trainer">regression</a></li>
                   <li>Relevance vector machines for <a href="ml.html#rvm_trainer">classification</a> 
@@ -128,6 +127,7 @@
                   <li>An online kernelized <a href="ml.html#kcentroid">centroid estimator</a>/novelty detector</li>
                   <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>  
                </ul>
             </li>
 

docs/docs/ml.xml

@@ -788,14 +788,18 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
             <p>
                Trains a <a href="#probabilistic_decision_function">probabilistic_decision_function</a> using 
                some sort of batch trainer object such as the <a href="#svm_nu_trainer">svm_nu_trainer</a> or
-               <a href="#rbf_network_trainer">rbf_network_trainer</a>.
+               <a href="#krr_trainer">krr_trainer</a>.
             </p>
-            The probability model is created by using the technique described in the paper:
+            The probability model is created by using the technique described in the following papers:
             <blockquote>
                 Probabilistic Outputs for Support Vector Machines and
                 Comparisons to Regularized Likelihood Methods by 
                 John C. Platt.  March 26, 1999
             </blockquote>
+            <blockquote>
+                A Note on Platt's Probabilistic Outputs for Support Vector Machines
+                by Hsuan-Tien Lin, Chih-Jen Lin, and Ruby C. Weng
+            </blockquote>
          </description>
          <examples>
             <example>svm_ex.cpp.html</example>
@@ -811,6 +815,9 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
          <spec_file link="true">dlib/svm/rbf_network_abstract.h</spec_file>
          <description>
                Trains a radial basis function network and outputs a <a href="#decision_function">decision_function</a>. 
+               It's worth pointing out that this object is essentially an unregularized version 
+               of <a href="#krr_trainer">kernel ridge regression</a>.  This means 
+               you should really prefer to use kernel ridge regression instead.
          </description>
       </component>
       

docs/docs/optimization.xml

@@ -40,6 +40,7 @@
          <name>Strategies</name>
          <item>cg_search_strategy</item>
          <item>bfgs_search_strategy</item>
+         <item>newton_search_strategy</item>
          <item>lbfgs_search_strategy</item>
          <item>objective_delta_stop_strategy</item>
          <item>gradient_norm_stop_strategy</item>
@@ -199,6 +200,29 @@
                                  
       </component>
 
+   <!-- ************************************************************************* -->
+      
+      <component>
+         <name>newton_search_strategy</name>
+         <file>dlib/optimization.h</file>
+         <spec_file link="true">dlib/optimization/optimization_search_strategies_abstract.h</spec_file>
+         <description>
+                This object represents a strategy for determining which direction
+                a <a href="#line_search">line search</a> should be carried out along.  This particular routine 
+                is an implementation of the newton method for determining this direction.  
+                That means using it requires you to supply a method for
+                creating hessian matrices for the problem you are trying to optimize.
+
+               <p>
+               Note also that this is actually a helper function for creating 
+               <a href="dlib/optimization/optimization_search_strategies_abstract.h.html#newton_search_strategy_obj"
+                     >newton_search_strategy_obj</a> objects.  
+               </p>
+
+         </description>
+                                 
+      </component>
+
    <!-- ************************************************************************* -->
       
       <component>

docs/docs/release_notes.xml

@@ -11,6 +11,35 @@
 <!-- *******************************************************************************  -->
 
 <current>
+New Stuff:
+  - Added a reference_wrapper implementation and modified the thread_function 
+    slightly so it works with it.
+  - Added an implementation of kernel ridge regression.
+  - Added a simple newton search strategy for optimizing functions.
+
+Non-Backwards Compatible Changes:
+  - If you have created your own matrix expressions then its possible this
+    new release will cause them to not compile.  
+
+Bug fixes:
+  - Fixed a bug in scale_columns.  It said it didn't have any destructive aliasing 
+    when in fact it destructively aliased its second argument.
+  - Fixed a bug in the random number generator where setting the seed back to ""
+    didn't result in the object going back to it's initial state.
+
+Other:
+  - Reorganized the matrix expression code.  It's now much simpler and the
+    library includes a new example program which details the steps needed to
+    create new matrix expressions.
+  - Changed the train_probabilistic_decision_function() routine so that it uses
+    a more numerically stable method to perform its maximum likelihood optimization.
+  - Added missing get/set epsilon functions to the RVM training objects.
+    I also changed the default epsilon from 0.0005 to 0.001.
+</current>
+
+<!-- *******************************************************************************  -->
+
+<old name="17.28" date="Jun 14, 2010">
 New Stuff:
    - Added the simplify_linear_decision_function() routines.
    - Added the find_approximate_k_nearest_neighbors() function.
@@ -33,7 +62,7 @@ Other:
    - Made the reduced() and reduced2() functions more efficient.
    - Many small usability improvements here and there.
 
-</current>
+</old>
 
 <!-- *******************************************************************************  -->
 

docs/docs/term_index.xml

@@ -54,6 +54,8 @@
          <term file="optimization.html" name="negate_function"/>
          <term file="optimization.html" name="cg_search_strategy"/>
          <term file="optimization.html" name="bfgs_search_strategy"/>
+         <term file="optimization.html" name="newton_search_strategy"/>
+         <term file="optimization.html" name="newton_search_strategy_obj"/>
          <term file="optimization.html" name="lbfgs_search_strategy"/>
 
          <term file="bayes.html" name="set_node_value"/>