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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">SVMCLASS</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>Support Vector Machines Classifier.</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> [y,dfce] = svmclass( X, model )</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> [y,dfce] = svmclass( X, model ) classifies input vectors X</span><br><span class=help> into classes using the multi-class SVM classifier</span><br><span class=help> y(i) = argmax f_j(X(:,i))</span><br><span class=help> j=1..nfun</span><br><span class=help> where f_j are linear functions in the feature space given </span><br><span class=help> by the prescribed kernel function (options.ker, options.arg). </span><br><span class=help> The discriminant functions f_j are determined by </span><br><span class=help> .Alpha [nsv x nfun] ... multipliers associated to SV</span><br><span class=help> .b [nclass] ... biases of discriminant functions.</span><br><span class=help> .sv.X [dim x nsv] ... support vectors.</span><br><span class=help> </span><br><span class=help> See 'help kernelproj' for more info about valuation of the </span><br><span class=help> discriminant functions f_j.</span><br><span class=help></span><br><span class=help> In the binary case nfun=1 the binary SVM classifier is used</span><br><span class=help> y(i) = 1 if f(X(:,i) >= 0</span><br><span class=help> = 2 if f(X(:,i) < 0</span><br><span class=help> where f is the disrimiant function given by Alpha [nsv x 1],</span><br><span class=help> b [1x1] and support vectors sv.X.</span><br><span class=help> </span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> X [dim x num_data] Input vectors to be classified.</span><br><span class=help></span><br><span class=help> model [struct] SVM classifier:</span><br><span class=help> .Alpha [nsv x nfun] Multipliers associated to suport vectors.</span><br><span class=help> .b [nfun x 1] Biases.</span><br><span class=help> .sv.X [dim x nsv] Support vectors.</span><br><span class=help> .options.ker [string] Kernel identifier.</span><br><span class=help> .options.arg [1 x nargs] Kernel argument(s).</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> y [1 x num_data] Predicted labels.</span><br><span class=help> dfce [nfun x num_data] Values of discriminant functions.</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> model = smo(trn,struct('ker','rbf','arg',1,'C',10));</span><br><span class=help> tst = load('riply_tst');</span><br><span class=help> ypred = svmclass( tst.X, model );</span><br><span class=help> cerror( ypred, tst.y )</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/svmquadprog.html" target="mdsbody">SVMQUADPROG</a>, <a href = "../kernels/kfd.html" target="mdsbody">KFD</a>, KFDQP, <a href = "../svm/mvsvmclass.html" target="mdsbody">MVSVMCLASS</a>. </span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../svm/list/svmclass.html">svmclass.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> 14-may-2004, VF<br> 09-May-2003, VF<br> 14-Jan-2003, VF<br></body></html>?? 快捷鍵說明
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