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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">EANDERS</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>Epsilon-solution of the Generalized Andersson'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 = eanders(distrib )</span><br><span class=help> model = eanders(distrib, options)</span><br><span class=help> model = eanders(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 Schlesinger's iterative</span><br><span class=help> algorithm which finds the epsilon-solution of the Generalized </span><br><span class=help> Anderson's task using the Kozinec's algorithm [<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] Input set of labeld (1 or 2) Gassians:</span><br><span class=help> .Mean [dim x ncomp] Mean veactors.</span><br><span class=help> .Cov [dim x dim x ncomp] Covariance matrices.</span><br><span class=help> .y [1 x ncomp] Labels of Gaussian (1 or 2).</span><br><span class=help> </span><br><span class=help> options [struct] Determine stopping conditions:</span><br><span class=help> .tmax [1x1] Maximal number of iterations.</span><br><span class=help> .err [1x1] Desired classification error; must be 0<err<0.5; </span><br><span class=help> (default 0.05).</span><br><span class=help></span><br><span class=help> init_model [struct] Initial model:</span><br><span class=help> W1, W2, t.</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Binary linear classifier:</span><br><span class=help> .W [dim x 1] Normal vector of the linear rule (hypeplane).</span><br><span class=help> .b [1x1] Bias of the rule (shift from the origin).</span><br><span class=help> </span><br><span class=help> .t [1x1] Number of used iterations.</span><br><span class=help> .exitflag [1x1] 1 ... solution with desired err was found.</span><br><span class=help> 0 ... maximal number of iterations exceeded.</span><br><span class=help> -1 ... solution does not exist.</span><br><span class=help> .W1, .W2 Auxciliary vectors; W=W1-W2.</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 = eanders(distrib,struct('err',0.06'));</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/ganders.html" target="mdsbody">GANDERS</a>, <a href = "../../linear/anderson/ggradandr.html" target="mdsbody">GGRADANDR</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/eanders.html">eanders.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> 21-may-2004, VF<br> 16-sep-2003, VF<br></body></html>?? 快捷鍵說明
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