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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>svm2.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>model </span>= <span class=defun_name>svm2</span>(<span class=defun_in>data,options</span>)<br><span class=h1>% SVM2 Learning of binary SVM classifier with L2-soft margin.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% model = svm2(data)</span><br><span class=help>% model = svm2(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 learns binary Support Vector Machines</span><br><span class=help>% classifier with L2-soft margin. The corresponding quadratic </span><br><span class=help>% programming task is solved by one of the following </span><br><span class=help>% <span class=help_field>algorithms:</span></span><br><span class=help>% mdm ... Mitchell-Demyanov-Malozemov (MDM) algorithm.</span><br><span class=help>% imdm ... Improved MDM algorithm.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Input:</span></span><br><span class=help>% data [struct] Training data:</span><br><span class=help>% .X [dim x num_data] Training vectors.</span><br><span class=help>% .y [1 x num_data] Labels must equal 1 and/or 2.</span><br><span class=help>%</span><br><span class=help>% options [struct] Control parameters:</span><br><span class=help>% .ker [string] Kernel identifier. See 'help kernel'.</span><br><span class=help>% .arg [1 x nargs] Kernel argument(s).</span><br><span class=help>% .C [1x1] Regularization constant.</span><br><span class=help>% .qp [string] QP solver to use: 'mdm', 'imdm' (default).</span><br><span class=help>% .tmax [1x1] Maximal number of iterations.</span><br><span class=help>% .tolabs [1x1] Absolute tolerance stopping condition (default 0.0).</span><br><span class=help>% .tolrel [1x1] Relative tolerance stopping condition (default 1e-3).</span><br><span class=help>% .cache [1x1] Number of columns of kernel matrix to be cached.</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 of support vectors.</span><br><span class=help>% .b [1x1] Bias of decision function.</span><br><span class=help>% .sv.X [dim x nsv] Support vectors.</span><br><span class=help>% .sv.inx [1 x nsv] Indices of SVs (model.sv.X = data.X(:,inx)).</span><br><span class=help>% .nsv [int] Number of Support Vectors.</span><br><span class=help>% .kercnt [1x1] Number of kernel evaluations.</span><br><span class=help>% .trnerr [1x1] Classification error on training data.</span><br><span class=help>% .options [struct] Copy of used options.</span><br><span class=help>% .cputime [1x1] Used CPU time in seconds (meassured by tic-toc).</span><br><span class=help>% .qp_stat [struct] Statistics about QP optimization:</span><br><span class=help>% .access [1x1] Number of requested columns of matrix H.</span><br><span class=help>% .t [1x1] Number of iterations.</span><br><span class=help>% .UB [1x1] Upper bound on optimal value of criterion. </span><br><span class=help>% .LB [1x1] Lower bound on optimal value of criterion. </span><br><span class=help>% .LB_History [1x(t+1)] LB with respect to iteration.</span><br><span class=help>% .UB_History [1x(t+1)] UB with respect to iteration.</span><br><span class=help>% .NA [1x1] Number of non-zero entries in 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',1);</span><br><span class=help>% model = svm2(data,options )</span><br><span class=help>% figure; ppatterns(data); psvm( model );</span><br><span class=help>%</span><br><span class=help>% See also</span><br><span class=help>% SVMCLASS, SVMLIGHT, SMO.</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) 1999-2004, 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>% 29-nov-2004, VF</span><br><br><hr><span class=comment>% restart clock</span><br>tic;<br><br><span class=keyword>if</span> <span class=stack>nargin</span> < 2, options = []; <span class=keyword>else</span> options = c2s(options); <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'qp'</span>), options.qp = <span class=quotes>'imdm'</span>; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'tolabs'</span>), options.tolabs = 0; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'tolrel'</span>), options.tolrel = 1e-3; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'tmax'</span>), options.tmax = inf; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'C'</span>), options.C = inf; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'ker'</span>), options.ker = <span class=quotes>'linear'</span>; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'arg'</span>), options.arg = 1; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'cache'</span>), options.cache = 1000; <span class=keyword>end</span><br><span class=keyword>if</span> ~isfield(options,<span class=quotes>'qp_verb'</span>), options.qp_verb = 0; <span class=keyword>end</span><br><br><span class=comment>% call MEX implementation of QPC2 solver</span><br>[Alpha,b,exitflag,kercnt,access,errcnt,t,UB,LB,History] = svm2_mex(...<br> data.X,...<br> data.y,...<br> options.ker,...<br> options.arg,...<br> options.C,...<br> options.qp,...<br> options.tmax,...<br> options.tolabs, ...<br> options.tolrel,...<br> options.cache, ...<br> options.qp_verb );<br><br><span class=comment>% remove non-support vectors</span><br>inx = find(Alpha ~=0 );<br><br><span class=comment>% setup output model</span><br>model.Alpha = Alpha(inx);<br>model.b = b;<br>model.sv.X = data.X(:,inx);<br>model.sv.inx = inx;<br>model.sv.y = data.y(inx);<br>model.nsv = length(inx);<br>model.options = options;<br>model.kercnt = kercnt;<br>model.trnerr = errcnt/size(data.X,2);<br>model.errcnt = errcnt;<br>model.qp_stat.access = access;<br>model.qp_stat.t = t;<br>model.qp_stat.UB = UB;<br>model.qp_stat.LB = LB;<br>model.qp_stat.LB_History = History(1,:);<br>model.qp_stat.UB_History = History(2,:);<br>model.qp_stat.NA = length(inx);<br>model.cputime = toc;<br>model.fun = <span class=quotes>'svmclass'</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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