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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">ANDRERR</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>Classification error of 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> [err,r,inx] = andrerr( model, distrib )</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function computes the classification error of</span><br><span class=help> the given linear classifier and underlying set of Gaussian </span><br><span class=help> distributions as defined in the Generalized Anderson's </span><br><span class=help> 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> <span class=help_field>Input:</span></span><br><span class=help> model [struct] Linear classifier:</span><br><span class=help> .W [dim x 1] Normal vector the separating hyperplane.</span><br><span class=help> .b [real] Bias the hyperplane.</span><br><span class=help> </span><br><span class=help> distrib [struct] Set of Gaussians with assigned binary labels:</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] Lables of Gaussians (1 or 2).</span><br><span class=help> </span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> err [real] Probability of misclassification.</span><br><span class=help> r [real] Mahalanobis distance of the cloasest Gaussian.</span><br><span class=help> inx [int] Index of the cloasest Gaussian.</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,{'err',0.06'});</span><br><span class=help> figure; pandr( model, distrib );</span><br><span class=help> error = andrerr( 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/eanders.html" target="mdsbody">EANDERS</a>, <a href = "../../linear/anderson/ggradandr.html" target="mdsbody">GGRADANDR</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../linear/anderson/list/andrerr.html">andrerr.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> 4-may-2004, VF<br> 17-sep-2003, VF<br></body></html>⌨️ 快捷键说明
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