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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">RSPOLY2</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>Reduced set method for second order homogeneous polynomial kernel.</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> red_model = rspoly2(model)</span><br><span class=help> red_model = rspoly2(model,max_nsv)</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 to reduce complexity</span><br><span class=help> of the kernel expansion with second order homogeneous polynomial </span><br><span class=help> kernel k(x,y) = (x'*y)^2 = kernel(x,y,'poly',2) .</span><br><span class=help></span><br><span class=help> The method was published in </span><br><span class=help> J.C.Burges: Simplified Support Vector Decision Rules. ICML, 1996.</span><br><span class=help> </span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> model [struct] Kernel expansion:</span><br><span class=help> .Alpha [nsv x 1] Weights of kernel expansion.</span><br><span class=help> .b [1x1] Bias.</span><br><span class=help> .sv.X [dim x nsv] Support vectors.</span><br><span class=help> .options.ker = 'poly'</span><br><span class=help> .options.arg = [2 0]</span><br><span class=help></span><br><span class=help> max_nsv [1x1] Maximal number of new support vectors. If not given </span><br><span class=help> then the new expansion approximates the original one exactly with</span><br><span class=help> at most dim support vectors. </span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> red_model [struct] Reduced kernel expansion:</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,struct('ker','poly','arg',[2 0],'C',10));</span><br><span class=help> red_model = rspoly2( model );</span><br><span class=help> figure; </span><br><span class=help> subplot(1,2,1); axis square; ppatterns(trn); psvm(model);</span><br><span class=help> subplot(1,2,2); axis square; ppatterns(trn); psvm(red_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 = "../kernels/rsrbf.html" target="mdsbody">RSRBF</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../kernels/list/rspoly2.html">rspoly2.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2004, 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> 22-dec-2004, VF, header and comments added<br> 28-nov-2003, VF<br></body></html>⌨️ 快捷键说明
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