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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">MVSVMCLASS</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>Majority voting multi-class SVM 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,votes] = mvsvmclass(X,model)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> [y,votes] = mvsvmclass(X,model) multi-class SVM classifier </span><br><span class=help> based on majority voting. The classifier involves nrule</span><br><span class=help> binary rules each classifying into one of nclass labels.</span><br><span class=help> The final decision is make for the class with majority </span><br><span class=help> votes.</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] Multi-class SVM majority voting classifier:</span><br><span class=help> .Alpha [nsv x nrule] Weights.</span><br><span class=help> .bin_y [2 x nrule] Translation between binary responses of</span><br><span class=help> the discriminant functions and class labels.</span><br><span class=help> .b [nrule x 1] Biases of discriminant functions.</span><br><span class=help> .sv.X [dim x nsv] Support vectors.</span><br><span class=help> .options.ker [string] Kernel identifier; see 'help kernel'.</span><br><span class=help> .options.arg [1 x nargs] Kernel agrument(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> votes [nclass x num_data] Number of votes for each class.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></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/oaosvm.html" target="mdsbody">OAOSVM</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/mvsvmclass.html">mvsvmclass.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></body></html>?? 快捷鍵說明
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