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<?xml version="1.0" encoding="utf-8"?>
<!-- $Revision$ -->
<reference xml:id="class.svm" role="class" xmlns="http://docbook.org/ns/docbook" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xi="http://www.w3.org/2001/XInclude">
<title>The SVM class</title>
<titleabbrev>SVM</titleabbrev>
<partintro>
<!-- {{{ svm intro -->
<section xml:id="svm.intro">
&reftitle.intro;
<para>
</para>
</section>
<!-- }}} -->
<section xml:id="svm.synopsis">
&reftitle.classsynopsis;
<!-- {{{ Synopsis -->
<classsynopsis>
<ooclass><classname>SVM</classname></ooclass>
<!-- {{{ Class synopsis -->
<classsynopsisinfo>
<ooclass>
<classname>SVM</classname>
</ooclass>
</classsynopsisinfo>
<!-- }}} -->
<classsynopsisinfo role="comment">&Constants;</classsynopsisinfo>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.c-svc">SVM::C_SVC</varname>
<initializer>0</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.nu-svc">SVM::NU_SVC</varname>
<initializer>1</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.one-class">SVM::ONE_CLASS</varname>
<initializer>2</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.epsilon-svr">SVM::EPSILON_SVR</varname>
<initializer>3</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.nu-svr">SVM::NU_SVR</varname>
<initializer>4</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.kernel-linear">SVM::KERNEL_LINEAR</varname>
<initializer>0</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.kernel-poly">SVM::KERNEL_POLY</varname>
<initializer>1</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.kernel-rbf">SVM::KERNEL_RBF</varname>
<initializer>2</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.kernel-sigmoid">SVM::KERNEL_SIGMOID</varname>
<initializer>3</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.kernel-precomputed">SVM::KERNEL_PRECOMPUTED</varname>
<initializer>4</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-type">SVM::OPT_TYPE</varname>
<initializer>101</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-kernel-type">SVM::OPT_KERNEL_TYPE</varname>
<initializer>102</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-degree">SVM::OPT_DEGREE</varname>
<initializer>103</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-shrinking">SVM::OPT_SHRINKING</varname>
<initializer>104</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-probability">SVM::OPT_PROPABILITY</varname>
<initializer>105</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-gamma">SVM::OPT_GAMMA</varname>
<initializer>201</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-nu">SVM::OPT_NU</varname>
<initializer>202</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-eps">SVM::OPT_EPS</varname>
<initializer>203</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-p">SVM::OPT_P</varname>
<initializer>204</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-coef-zero">SVM::OPT_COEF_ZERO</varname>
<initializer>205</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-c">SVM::OPT_C</varname>
<initializer>206</initializer>
</fieldsynopsis>
<fieldsynopsis>
<modifier>const</modifier>
<type>int</type>
<varname linkend="svm.constants.opt-cache-size">SVM::OPT_CACHE_SIZE</varname>
<initializer>207</initializer>
</fieldsynopsis>
<classsynopsisinfo role="comment">&Methods;</classsynopsisinfo>
<xi:include xpointer="xmlns(db=http://docbook.org/ns/docbook) xpointer(id('class.svm')/db:refentry/db:refsect1[@role='description']/descendant::db:constructorsynopsis[not(@role='procedural')])">
<xi:fallback/>
</xi:include>
<xi:include xpointer="xmlns(db=http://docbook.org/ns/docbook) xpointer(id('class.svm')/db:refentry/db:refsect1[@role='description']/descendant::db:methodsynopsis[not(@role='procedural')])">
<xi:fallback/>
</xi:include>
</classsynopsis>
<!-- }}} -->
</section>
<!-- {{{ svm constants -->
<section xml:id="svm.constants">
&reftitle.constants;
<section xml:id="svm.constants.types">
<title>SVM Constants</title>
<variablelist>
<varlistentry xml:id="svm.constants.c-svc">
<term><constant>SVM::C_SVC</constant></term>
<listitem>
<para>The basic C_SVC SVM type. The default, and a good starting point</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.nu-svc">
<term><constant>SVM::NU_SVC</constant></term>
<listitem>
<para>The NU_SVC type uses a different, more flexible, error weighting</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.one-class">
<term><constant>SVM::ONE_CLASS</constant></term>
<listitem>
<para>One class SVM type. Train just on a single class, using outliers as negative examples</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.epsilon-svr">
<term><constant>SVM::EPSILON_SVR</constant></term>
<listitem>
<para>A SVM type for regression (predicting a value rather than just a class)</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.nu-svr">
<term><constant>SVM::NU_SVR</constant></term>
<listitem>
<para>A NU style SVM regression type</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.kernel-linear">
<term><constant>SVM::KERNEL_LINEAR</constant></term>
<listitem>
<para>A very simple kernel, can work well on large document classification problems</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.kernel-poly">
<term><constant>SVM::KERNEL_POLY</constant></term>
<listitem>
<para>A polynomial kernel</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.kernel-rbf">
<term><constant>SVM::KERNEL_RBF</constant></term>
<listitem>
<para>The common Gaussian RBD kernel. Handles non-linear problems well and is a good default for classification</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.kernel-sigmoid">
<term><constant>SVM::KERNEL_SIGMOID</constant></term>
<listitem>
<para>A kernel based on the sigmoid function. Using this makes the SVM very similar to a two layer sigmoid based neural network</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.kernel-precomputed">
<term><constant>SVM::KERNEL_PRECOMPUTED</constant></term>
<listitem>
<para>A precomputed kernel - currently unsupported.</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-type">
<term><constant>SVM::OPT_TYPE</constant></term>
<listitem>
<para>The options key for the SVM type</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-kernel-type">
<term><constant>SVM::OPT_KERNEL_TYPE</constant></term>
<listitem>
<para>The options key for the kernel type</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-degree">
<term><constant>SVM::OPT_DEGREE</constant></term>
<listitem>
<para></para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-shrinking">
<term><constant>SVM::OPT_SHRINKING</constant></term>
<listitem>
<para>Training parameter, boolean, for whether to use the shrinking heuristics</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-probability">
<term><constant>SVM::OPT_PROBABILITY</constant></term>
<listitem>
<para>Training parameter, boolean, for whether to collect and use probability estimates</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-gamma">
<term><constant>SVM::OPT_GAMMA</constant></term>
<listitem>
<para>Algorithm parameter for Poly, RBF and Sigmoid kernel types.</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-nu">
<term><constant>SVM::OPT_NU</constant></term>
<listitem>
<para>The option key for the nu parameter, only used in the NU_ SVM types</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-eps">
<term><constant>SVM::OPT_EPS</constant></term>
<listitem>
<para>The option key for the Epsilon parameter, used in epsilon regression</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-p">
<term><constant>SVM::OPT_P</constant></term>
<listitem>
<para>Training parameter used by Episilon SVR regression</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-coef-zero">
<term><constant>SVM::OPT_COEF_ZERO</constant></term>
<listitem>
<para>Algorithm parameter for poly and sigmoid kernels</para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-c">
<term><constant>SVM::OPT_C</constant></term>
<listitem>
<para>The option for the cost parameter that controls tradeoff between errors and generality - effectively the penalty for misclassifying training examples. </para>
</listitem>
</varlistentry>
<varlistentry xml:id="svm.constants.opt-cache-size">
<term><constant>SVM::OPT_CACHE_SIZE</constant></term>
<listitem>
<para>Memory cache size, in MB</para>
</listitem>
</varlistentry>
</variablelist>
</section>
</section>
<!-- }}} -->
</partintro>
&reference.svm.entities.svm;
</reference>
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