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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">SVMQUADPROG</b><td valign="baseline" align="right" class="function"><a href="../svm/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>SVM trained by Matlab Optimization Toolbox.</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 = svmquadprog( data )</span><br><span class=help> model = svmquadprog( data, options )</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function trains binary Support Vector Machines classifer </span><br><span class=help> with L1 or L2-soft margin. The SVM quadratic programming task </span><br><span class=help> is solved by the 'quadprog.m' of the Matlab Optimization toolbox.</span><br><span class=help></span><br><span class=help> See 'help svmclass' to see how to classify data with found classifier.</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 data:</span><br><span class=help> .X [dim x num_data] Vectors.</span><br><span class=help> .y [1 x num_data] Training labels.</span><br><span class=help></span><br><span class=help> options [struct] Control parameters:</span><br><span class=help> .ker [string] Kernel identifier (default 'linear'). </span><br><span class=help> See 'help kernel' for more info.</span><br><span class=help> .arg [1 x nargs] Kernel argument(s).</span><br><span class=help> .C SVM regularization constant (default inf):</span><br><span class=help> [1 x 1] .. the same for all training vectors.</span><br><span class=help> [1 x 2] .. for each class separately C=[C1,C2],</span><br><span class=help> [1 x num_data] .. each training vector separately.</span><br><span class=help> .norm [1x1] 1 .. L1-soft margin penalization (default).</span><br><span class=help> 2 .. L2-soft margin penalization.</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Binary SVM classifier:</span><br><span class=help> .Alpha [nsv x 1] Weights.</span><br><span class=help> .b [1x1] Bias of the decision function.</span><br><span class=help> .sv.X [dim x nsv] Support vectors.</span><br><span class=help> .nsv [1x1] Number of support vectors.</span><br><span class=help> .kercnt [1x1] Number of used kernel evaluations.</span><br><span class=help> .trnerr [1x1] Training classification error.</span><br><span class=help> .margin [1x1] Margin of found classifier.</span><br><span class=help> .cputime [1x1] Used CPU time in seconds.</span><br><span class=help> .options [struct] Copy of used options.</span><br><span class=help> .exitflag [1x1] Exitflag of the QUADPROG function. </span><br><span class=help> (if > 0 then it has converged to the solution).</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> data = load('riply_trn');</span><br><span class=help> options = struct('ker','rbf','arg',1,'C',10);</span><br><span class=help> model = svmquadprog(data,options)</span><br><span class=help> figure; ppatterns(data); psvm(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 = "../svm/smo.html" target="mdsbody">SMO</a>, <a href = "../svm/svmlight.html" target="mdsbody">SVMLIGHT</a>, <a href = "../svm/svmclass.html" target="mdsbody">SVMCLASS</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../svm/list/svmquadprog.html">svmquadprog.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> 31-may-2004, VF<br> 16-may-2004, VF<br> 17-Feb-2003, VF<br> 28-Nov-2001, VF, used quadprog instead of qp<br> 23-Occt-2001, VF<br> 19-September-2001, V. Franc, renamed to svmmot.<br> 8-July-2001, V.Franc, comments changed, bias mistake removed.<br> 28-April-2001, V.Franc, flps counter added<br> 10-April-2001, V. Franc, created<br></body></html>?? 快捷鍵說明
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