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<html><head> <meta HTTP-EQUIV="Content-Type" CONTENT="text/html;charset=ISO-8859-1"> <title>Contents.m</title><link rel="stylesheet" type="text/css" href="../stpr.css"></head><body><table border=0 width="100%" cellpadding=0 cellspacing=0><tr valign="baseline"><td valign="baseline" class="function"><b class="function">LIN2SVM</b><td valign="baseline" align="right" class="function"><a href="../kernels/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Merges linear rule and kernel projection.</b></p> <hr><div class='code'><code><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> <span class=also_field>See also </span><span class=also></span><br><span class=help><span class=also> <a href = "../quadrat/lin2quad.html" target="mdsbody">LIN2QUAD</a>, <a href = "../svm/svmclass.html" target="mdsbody">SVMCLASS</a>, <a href = "../linear/linclass.html" target="mdsbody">LINCLASS</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../kernels/list/lin2svm.html">lin2svm.m</a> <p><b class="info_field">(c) </b> Statistical Pattern Recognition Toolbox, (C) 1999-2003,<br> Written by Vojtech Franc and Vaclav Hlavac,<br> <a href="http://www.cvut.cz">Czech Technical University Prague</a>,<br> <a href="http://www.feld.cvut.cz">Faculty of Electrical engineering</a>,<br> <a href="http://cmp.felk.cvut.cz">Center for Machine Perception</a><br> <p><b class="info_field">Modifications: </b> <br> 10-jun-2004, VF<br> 02-Feb-2003, VF<br></body></html>⌨️ 快捷键说明
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