Commit 937f61b8 authored by Davis King's avatar Davis King

Reorganized the stuff on the algorithms page. I pulled the machine learning

and bayesian networking stuff out and gave them their own pages.

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extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402933
parent 0955ec45
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......@@ -26,7 +26,9 @@
short section about the component you would like to use. Then pick which implementation you
would like and typedef it to something nice. Here is an example of creating a typedef for
a set of integers using the first kernel implementation. <br/>
<tt>typedef dlib::set&lt;int&gt;::kernel_1a set_of_ints;</tt>
<tt>typedef dlib::set&lt;int&gt;::kernel_1a set_of_ints;</tt>. Or as another example,
if you wanted to make an expandable array of ints you might say <br/>
<tt>typedef dlib::array&lt;int&gt;::expand_1d_c array_of_ints;</tt>.
</p>
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......@@ -117,18 +117,22 @@
<li><b>Machine Learning Algorithms</b>
<ul>
<li><a href="algorithms.html#mlp">Multi layer perceptrons</a> </li>
<li><a href="algorithms.html#svm_nu_trainer">Support vector machines</a> for classification</li>
<li>Relevance vector machines for <a href="algorithms.html#rvm_trainer">classification</a>
and <a href="algorithms.html#rvm_regression_trainer">regression</a> </li>
<li>An online <a href="algorithms.html#krls">kernel RLS regression</a> algorithm</li>
<li>An online <a href="algorithms.html#svm_pegasos">SVM classification</a> algorithm</li>
<li>An online kernelized <a href="algorithms.html#kcentroid">centroid estimator</a>/novelty detector</li>
<li>A kernelized <a href="algorithms.html#kkmeans">k-means</a> clustering algorithm</li>
<li><a href="algorithms.html#rbf_network_trainer">Radial Basis Function Networks</a></li>
<li>Bayesian network inference algorithms such as the
<a href="algorithms.html#bayesian_network_join_tree">join tree</a> algorithm and
<a href="algorithms.html#bayesian_network_gibbs_sampler">gibbs sampler</a> markov chain monte carlo algorithm</li>
<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>Relevance vector machines for <a href="ml.html#rvm_trainer">classification</a>
and <a href="ml.html#rvm_regression_trainer">regression</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>
<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>
</ul>
</li>
<li><b>Bayesian Network Inference Algorithms</b>
<ul>
<li><a href="bayes.html#bayesian_network_join_tree">join tree</a> algorithm for exact inference</li>
<li><a href="bayes.html#bayesian_network_gibbs_sampler">gibbs sampler</a> markov chain monte carlo algorithm</li>
</ul>
</li>
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......@@ -10,6 +10,16 @@
<link>algorithms.html</link>
<chm_sub>algorithms.xml</chm_sub>
</item>
<item>
<name>Machine Learning</name>
<link>ml.html</link>
<chm_sub>ml.xml</chm_sub>
</item>
<item>
<name>Bayesian Nets</name>
<link>bayes.html</link>
<chm_sub>bayes.xml</chm_sub>
</item>
<item>
<name>Containers</name>
<link>containers.html</link>
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......@@ -57,6 +57,7 @@
<item>toupper</item>
<item>convert_utf8_to_utf32</item>
<item>is_combining_char</item>
<item>strings_equal_ignore_case</item>
</section>
</top>
</menu>
......@@ -216,6 +217,18 @@
</component>
<!-- ************************************************************************* -->
<component>
<name>strings_equal_ignore_case</name>
<file>dlib/string.h</file>
<spec_file link="true">dlib/string/string_abstract.h</spec_file>
<description>
This is a pair of functions to do a case insensitive comparison between strings.
</description>
</component>
<!-- ************************************************************************* -->
<component>
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