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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>Contents.m</title><link rel="stylesheet" type="text/css" href="../../stpr.css"></head><body><table border=0 width="100%" cellpadding=0 cellspacing=0><tr valign="baseline"><td valign="baseline" class="function"><b class="function">MLSIGMOID</b><td valign="baseline" align="right" class="function"><a href="../../probab/estimation/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table> <p><b>Fitting a sigmoid function using ML estimation.</b></p> <hr><div class='code'><code><span class=help></span><br><span class=help> <span class=help_field>Synopsis:</span></span><br><span class=help> model = mlsigmoid(data,options)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> model = mlsigmoid(data,options) computes Maximum-Likelihood</span><br><span class=help> estimation of parameters of sigmoid function [<a href="../../references.html#Platt99a" title = "" >Platt99a</a>]</span><br><span class=help> p(y==1|x) = 1/(1+exp(A(1)*x+A(2))),</span><br><span class=help></span><br><span class=help> used to describe a posteriory probability of a hidden binary </span><br><span class=help> state y from {1,2}. The conditional probabilities p(x|y) are </span><br><span class=help> assumed to be uni-variate Gaussian distribution. The training </span><br><span class=help> samples {(X(1),y(1)),...,(X(num_data),y(num_data))} assumed to </span><br><span class=help> be i.i.d. are given in data.X and data.y.</span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> data [struct] Input sample:</span><br><span class=help> .X [1 x num_data] Values of discriminant function.</span><br><span class=help> .y [1 x num_data] Corresponding class label (1 or 2).</span><br><span class=help></span><br><span class=help> options [struct] Control parameters:</span><br><span class=help> .regul [1x1] If 1 then fitting is regularized to prevent </span><br><span class=help> overfitting (default 1).</span><br><span class=help> .verb [1x1] If 1 then progress info is displayed (default 0).</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model.A [1x2] Parameters of sigmoid function.</span><br><span class=help> model.logl [1x1] Value of the log-likelihood criterion.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> help demo_svmpout;</span><br><span class=help></span><br><span class=help> <span class=also_field>See also </span><span class=also></span><br><span class=help><span class=also> <a href = "../../probab/sigmoid.html" target="mdsbody">SIGMOID</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../probab/estimation/list/mlsigmoid.html">mlsigmoid.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac<br> <a href="http://www.cvut.cz">Czech Technical University Prague</a><br> <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a><br> <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a><br> <p><b class="info_field">Modifications: </b> <br> 03-jun-2004, VF<br> 11-oct-2003, VF<br> 20-sep-2003, VF<br> 08-may-2003, VF<br></body></html>⌨️ 快捷键说明
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