📄 lin2svm.html
字号:
<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>lin2svm.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>svm_model </span>= <span class=defun_name>lin2svm</span>(<span class=defun_in>kfe_model, lin_model</span>)<br><span class=h1>% LIN2SVM Merges linear rule and kernel projection.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Synopsis:</span></span><br><span class=help>% svm_model = lin2svm(kfe_model,lin_model)</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Description:</span></span><br><span class=help>% This function merges kernel feature extraction model</span><br><span class=help>% (data-type kernel projection) and linear classifier to </span><br><span class=help>% create kernel (SVM) classifier.</span><br><span class=help>%</span><br><span class=help>% <span class=help_field>Input:</span></span><br><span class=help>% kfe_model [struct] Kernel data projection:</span><br><span class=help>% .Alpha [nsv x new_dim] Weight vector.</span><br><span class=help>% .b [new_dim x 1] Biases.</span><br><span class=help>% .oprions.ker [string] Kernel identifier (see 'help kernel').</span><br><span class=help>% .options.arg [1xnargs] Kernel arguments.</span><br><span class=help>%</span><br><span class=help>% lin_model [struct] Linear classifier:</span><br><span class=help>% .W [dim x nfun] Weight vector(s).</span><br><span class=help>% .b [nfun x 1] Bias(es).</span><br><span class=help>% </span><br><span class=help>% <span class=help_field>Output:</span></span><br><span class=help>% svm_model [struct] Kernel classifer:</span><br><span class=help>% .Alpha [nsv x nfun] Weight vector(s).</span><br><span class=help>% .b [nfun x 1] Bias(es).</span><br><span class=help>% .options [struct] Copy of kfe_model.options.</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,'new_dim',10);</span><br><span class=help>% kpca_model = greedykpca(data.X,options);</span><br><span class=help>% proj_data = kernelproj(data,kpca_model);</span><br><span class=help>% lin_model = fld(proj_data);</span><br><span class=help>% kfd_model = lin2svm(kpca_model,lin_model);</span><br><span class=help>% figure; ppatterns(data); pboundary(kfd_model);</span><br><span class=help>%</span><br><span class=help>% See also </span><br><span class=help>% LIN2QUAD, SVMCLASS, LINCLASS.</span><br><span class=help>%</span><br><hr><span class=help1>% <span class=help1_field>(c)</span> Statistical Pattern Recognition Toolbox, (C) 1999-2003,</span><br><span class=help1>% 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>% 10-jun-2004, VF</span><br><span class=help1>% 02-Feb-2003, VF</span><br><br><hr>svm_model.Alpha = kfe_model.Alpha*lin_model.W;<br>svm_model.b = lin_model.b+lin_model.W'*kfe_model.b;<br><br>svm_model.sv.X = kfe_model.sv.X;<br>svm_model.nsv = size(svm_model.sv.X,2);<br>svm_model.options = kfe_model.options;<br>svm_model.fun = <span class=quotes>'svmclass'</span>;<br><br><span class=jump>return</span>;<br><span class=comment>% EOF</span><br></code>
⌨️ 快捷键说明
复制代码
Ctrl + C
搜索代码
Ctrl + F
全屏模式
F11
切换主题
Ctrl + Shift + D
显示快捷键
?
增大字号
Ctrl + =
减小字号
Ctrl + -