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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>svmclass.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,dfce] </span>= <span class=defun_name>svmclass</span>(<span class=defun_in>X,model</span>)<br><span class=h1>% SVMCLASS Support Vector Machines Classifier.</span><br><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>% See also </span><br><span class=help>% SMO, SVMLIGHT, SVMQUADPROG, KFD, KFDQP, MVSVMCLASS. </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><span class=help1>% <span class=help1_field>Modifications:</span></span><br><span class=help1>% 14-may-2004, VF</span><br><span class=help1>% 09-May-2003, VF</span><br><span class=help1>% 14-Jan-2003, VF</span><br><br><hr><span class=comment>% allows model to be given in cell</span><br>model=c2s(model);<br><br>dfce = kernelproj(X, model);<br>nfun = size(dfce,1);<br><br><span class=keyword>if</span> nfun == 1,<br> <span class=comment>% Binary case</span><br> <span class=comment>%-------------------------------</span><br><br> y = ones(size(dfce));<br> y( find( dfce < 0 )) = 2;<br><br><span class=keyword>else</span> <br> <span class=comment>% Multi-class case</span><br> <span class=comment>%-------------------------------</span><br><br> [dummy,y] = max( dfce );<br><span class=keyword>end</span><br><br><span class=jump>return</span>;<br><span class=comment>% EOF</span><br><br></code>?? 快捷鍵說明
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