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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">GGRADANDER</b><td valign="baseline" align="right" class="function"><a href="../../linear/anderson/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table> <p><b>Gradient method to solve the Generalized Anderson's task.</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 = ggradandr( distrib)</span><br><span class=help> model = ggradandr( distrib, options)</span><br><span class=help> model = ggradandr( distrib, options, init_model)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function is an implementation of the algorithm</span><br><span class=help> using the generalized gradient optimization to solve</span><br><span class=help> the the Generalized Anderson's task [<a href="../../references.html#SH10" title = "M.I.Schlesinger and V.Hlavac. Ten lectures on statistical and structural pattern recognition. Kluwer Academic Publishers, 2002." >SH10</a>].</span><br><span class=help></span><br><span class=help> The goal of the GAT is find the binary linear classification</span><br><span class=help> rule (g(x)=sgn(W'*x+b) with minimal probability of </span><br><span class=help> misclassification. The conditional probabilities are known to </span><br><span class=help> be Gaussians their paramaters belong to a given set of parameters. </span><br><span class=help> The true parameters are not known. The linear rule which </span><br><span class=help> guarantes the minimimal classification error for the worst </span><br><span class=help> possible case (the worst configuration of Gaussains) is </span><br><span class=help> sought for.</span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> distrib [struct] Binary labeled Gaussian distributions:</span><br><span class=help> .Mean [dim x ncomp] Mean vectors.</span><br><span class=help> .Cov [dim x dim x ncomp] Covariance matrices.</span><br><span class=help> .y [1 x ncomp] labels of Gaussians (1 or 2).</span><br><span class=help></span><br><span class=help> options [struct] Defines stopping condition: </span><br><span class=help> .tmax [1x1] Maximal number of iterations (default 1e4 ).</span><br><span class=help> .eps [1x1] Minimal change in the optimised criterion (default 0).</span><br><span class=help></span><br><span class=help> init_model [struct] Initial model:</span><br><span class=help> .W, .b, .t see below.</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Linear classifier:</span><br><span class=help> .W [dim x 1] Normal vector of the found hypeprlane W'*x + b = 0.</span><br><span class=help> .b [1x1] Bias of the hyperplane.</span><br><span class=help></span><br><span class=help> .r [1x1] Mahalanobis distance for the cloasest Gaussian.</span><br><span class=help> .err [1x1] Probability of misclassification.</span><br><span class=help> .t [1x1] Number of iterations.</span><br><span class=help> .exitflag [1x1] 0 ... maximal number of iterations exceeded.</span><br><span class=help> 1 ... condition abs( r - old_r) < eps fulfilled.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> distrib = load('mars');</span><br><span class=help> model = ggradandr( distrib );</span><br><span class=help> figure; pandr( model, distrib );</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 = "../../linear/anderson/androrig.html" target="mdsbody">ANDRORIG</a>, <a href = "../../linear/anderson/eanders.html" target="mdsbody">EANDERS</a>, <a href = "../../linear/anderson/ganders.html" target="mdsbody">GANDERS</a>, <a href = "../../linear/anderson/andrerr.html" target="mdsbody">ANDRERR</a>, <a href = "../../linear/linclass.html" target="mdsbody">LINCLASS</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../linear/anderson/list/ggradandr.html">ggradandr.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> 17-sep-2003, VF<br></body></html>?? 快捷鍵說明
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