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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">FLDQP</b><td valign="baseline" align="right" class="function"><a href="../../linear/fisher/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table> <p><b>Fisher Linear Discriminat using Quadratic Programming.</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 = fldqp( data )</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 binary linear classifier based</span><br><span class=help> on the Fisher Linear Discriminant (FLD) using the Quadratic</span><br><span class=help> Programming (quadprog) optimization. The inputs are</span><br><span class=help> binary labeled training vectors. The parameter vector W</span><br><span class=help> of the linear classifier</span><br><span class=help> q(x) = 1 for W'*x + b >= 0</span><br><span class=help> = 2 for W'*x + b < 0</span><br><span class=help> </span><br><span class=help> is computed to maximize class separability criterion.</span><br><span class=help> The bias b is determined to lie between means of training</span><br><span class=help> data projected onto direction W. </span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> data [struct] Binary labeled training vectors.</span><br><span class=help> .X [dim x num_data] Training vectors.</span><br><span class=help> .y [1 x num_data] Labels (1 or 2).</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] Parameter vector the linear classifier.</span><br><span class=help> .b [1x1] Bias of the linear classifier.</span><br><span class=help> .separab [1x1] Meassure of class separability.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> trn = load('riply_trn');</span><br><span class=help> tst = load('riply_tst');</span><br><span class=help> model = fldqp( trn );</span><br><span class=help> ypred = linclass( tst.X, model);</span><br><span class=help> cerror(ypred, tst.y)</span><br><span class=help> figure; ppatterns(trn); pline(model);</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/fisher/fld.html" target="mdsbody">FLD</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/fisher/list/fldqp.html">fldqp.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> 1-may-2004, VF<br> 30-apr-2004, VF<br> 24-Feb-2003, VF<br> 1-Feb-2003, VF<br></body></html>?? 快捷鍵說(shuō)明
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