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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>mvsvmclass.m</title><link rel="stylesheet" type="text/css" href="../../m-syntax.css"></head><body><code><span class=defun_kw>function</span> <span class=defun_out>[y,votes] </span>= <span class=defun_name>mvsvmclass</span>(<span class=defun_in>X,model</span>)<br><span class=h1>% MVSVMCLASS Majority voting multi-class SVM classifier.</span><br><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>% See also </span><br><span class=help>% OAOSVM, SVMCLASS.</span><br><span class=help>%</span><br><hr><span class=help1>% <span class=help1_field>About:</span> Statistical Pattern Recognition Toolbox</span><br><span class=help1>% (C) 1999-2003, Written by Vojtech Franc and Vaclav Hlavac</span><br><span class=help1>% <a href="http://www.cvut.cz">Czech Technical University Prague</a></span><br><span class=help1>% <a href="http://www.feld.cvut.cz">Faculty of Electrical Engineering</a></span><br><span class=help1>% <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a></span><br><br><hr><span class=comment>% Modifications</span><br><span class=comment>% 11-Feb-2003, VF </span><br><span class=comment>% 8-Feb-2003, VF </span><br><span class=comment>% 3-Jun-2002, V.Franc</span><br><br>[dim,num_data] = size(X);<br>nclass = max( model.bin_y(:) );<br>nrule = size( model.Alpha, 2);<br><br>votes = zeros(nclass, num_data );<br><br>dfce = kernelproj( X, model );<br><br><span class=keyword>for</span> i=1:nrule,<br> <br> inx_pos = find( dfce(i,:) >= 0 );<br> inx_neg = find( dfce(i,:) < 0 );<br><br> votes( model.bin_y(1,i), inx_pos) = votes( model.bin_y(1,i), inx_pos) + 1;<br> votes( model.bin_y(2,i), inx_neg) = votes( model.bin_y(2,i), inx_neg) + 1;<br><br><span class=keyword>end</span><br><br>[dummy, y] = max( votes );<br><br><span class=jump>return</span>;<br><span class=comment>% EOF</span><br></code>?? 快捷鍵說明
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