<?xml version="1.0" encoding='utf-8'?>
<!DOCTYPE wml PUBLIC "-//WAPFORUM//DTD WML 1.1//EN" "http://www.wapforum.org/DTD/wml_1.1.xml">
<wml>
<card id="card1" title="Stochastic gradient descent - Page 5 - Wikipedia">
<p>
<a accesskey="1" href="page.php?w=Stochastic_gradient_descent&amp;p=4">1.Previous</a><br />
<a accesskey="3" href="page.php?w=Stochastic_gradient_descent&amp;p=6">3.Next</a>
</p>
<p>(for independent observations).  The general class of estimators that arise as minimizers of sums are called <a href="page.php?w=M-estimator">M-estimator</a>s. However, in statistics, it has been long recognized that requiring even local minimization is too restrictive for some problems of maximum-likelihood estimation. Therefore, contemporary statistical theorists often consider <a href="page.php?w=stationary_point">stationary point</a>s of the <a href="page.php?w=likelihood_function">likelihood function</a> (or zeros of its derivative, the</p><p>
<a accesskey="1" href="page.php?w=Stochastic_gradient_descent&amp;p=4">1.Previous</a><br />
<a accesskey="3" href="page.php?w=Stochastic_gradient_descent&amp;p=6">3.Next</a>
</p>

<do type="prev" label="Search">
        <go href="search.wml"/>
</do>

</card>
</wml>
