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 <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-propability">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-propability">
      <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;

</phpdoc:classref>

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