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<title>Create a SVMModel based on training data</title>
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<h1 class="refname">SVM::train</h1>
<p class="verinfo">(PECL svm >= 0.1.0)</p><p class="refpurpose"><span class="refname">SVM::train</span> — <span class="dc-title">Create a SVMModel based on training data</span></p>
</div>
<div class="refsect1 description" id="refsect1-svm.train-description">
<h3 class="title">Description</h3>
<div class="methodsynopsis dc-description">
<span class="modifier">public</span> <span class="type"><a href="class.svmmodel.html" class="type SVMModel">SVMModel</a></span> <span class="methodname"><strong>svm::train</strong></span>
( <span class="methodparam"><span class="type">array</span> <code class="parameter">$problem</code></span>
[, <span class="methodparam"><span class="type">array</span> <code class="parameter">$weights</code></span>
] )</div>
<p class="para rdfs-comment">
Train a support vector machine based on the supplied training data.
</p>
</div>
<div class="refsect1 parameters" id="refsect1-svm.train-parameters">
<h3 class="title">Parameters</h3>
<p class="para">
<dl>
<dt>
<span class="term"><em><code class="parameter">problem</code></em></span>
<dd>
<p class="para">
The problem can be provided in three different ways.
An array, where the data should start with the class label
(usually 1 or -1) then followed by a sparse data set of
dimension => data pairs.
A URL to a file containing a SVM Light formatted problem, with the
each line being a new training example, the start of each line
containing the class (1, -1) then a series of tab separated data
values shows as key:value.
A opened stream pointing to a data source formatted as in the file above.
</p>
</dd>
</dt>
<dt>
<span class="term"><em><code class="parameter">weights</code></em></span>
<dd>
<p class="para">
Weights are an optional set of weighting parameters for the different
classes, to help account for unbalanced training sets. For example,
if the classes were 1 and -1, and -1 had significantly more example
than one, the weight for -1 could be 0.5. Weights should be in the range 0-1.
</p>
</dd>
</dt>
</dl>
</p>
</div>
<div class="refsect1 returnvalues" id="refsect1-svm.train-returnvalues">
<h3 class="title">Return Values</h3>
<p class="para">
Returns an SVMModel that can be used to classify previously unseen data.
Throws SVMException on error
</p>
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