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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>redquadh.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>red_model </span>= <span class=defun_name>redquadh</span>(<span class=defun_in>model</span>)<br><span class=h1>% REDQUADH reduced SVM classifier with homogeneous quadratic kernel.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% red_model = redquadh(model)</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Description:</span></span><br><span class=help>% It uses reduced set techique (Burges) to compute </span><br><span class=help>% simpler SVM binary rule with homogeneous quadratic kernel (x'*y)^2.</span><br><span class=help>% </span><br><span class=help>% <span class=help_field>Input:</span></span><br><span class=help>% model.Alpha [nsv x 1] Weights of kernel expansion.</span><br><span class=help>% model.b [scalar] Bias.</span><br><span class=help>% model.sv.X [dim x nsv] Support vectors.</span><br><span class=help>% model.options.ker = 'poly'</span><br><span class=help>% model.options.arg = [2 0]</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Output:</span></span><br><span class=help>% red_model.Alpha [new_nsv x 1] New weights.</span><br><span class=help>% red_model.b [scalar] Bias.</span><br><span class=help>% red_model.sv.X [dim x new_nsv] New "support vectors".</span><br><span class=help>% ...</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,{'ker','poly','arg',[2 0],'C',10});</span><br><span class=help>% red_model = redquadh( model );</span><br><span class=help>% figure; ppatterns(trn); psvm(model);</span><br><span class=help>% figure; ppatterns(trn); psvm(red_model);</span><br><span class=help>%</span><br><hr><span class=help1>% <span class=help1_field>Modifications:</span></span><br><span class=help1>% 28-nov-2003, VF</span><br><br><hr>dim=size(model.sv.X,1);<br>nsv = model.nsv;<br><br>S = zeros(dim,dim);<br><br><span class=keyword>for</span> i=1:dim,<br> <span class=keyword>for</span> j=i:dim,<br> S(i,j) = (model.sv.X(i,:).*model.sv.X(j,:) )*model.Alpha(:);<br> S(j,i) = S(i,j);<br> <span class=keyword>end</span><br><span class=keyword>end</span><br><br>[V,D] = eig(S);<br>D=real(diag(D));<br>[dummy,inx] = sort(-abs(D));<br>D=D(inx);<br>V=V(:,inx);<br><br>inx = find(D ~= 0);<br><br>red_model.nsv = length(inx);<br>red_model.Alpha = zeros(red_model.nsv,1);<br>red_model.b = model.b;<br>red_model.sv.X = zeros(dim,red_model.nsv);<br>red_model.options = model.options;<br>red_model.classifier = <span class=quotes>'svmclass'</span>;<br>red_model.eigval = D(inx);<br><br>cnt = 0;<br><span class=keyword>for</span> i=inx(:)',<br> cnt = cnt+1;<br> red_model.sv.X(:,cnt) = V(:,i);<br> red_model.Alpha(cnt) = D(i)/(red_model.sv.X(:,cnt)'*red_model.sv.X(:,cnt));<br><span class=keyword>end</span><br><br><span class=jump>return</span>;<br><br><br></code>?? 快捷鍵說明
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