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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">GREEDYKLS</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>Greedy Regularized Kernel Least Squares.</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> model = greedykls(X)</span><br><span class=help> model = greedykls(X,options)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function approximates input vectors X in the feature</span><br><span class=help> space using GREEDYKPCA. Then the regularized least squares</span><br><span class=help> are applied on the approximated data. </span><br><span class=help></span><br><span class=help> See help of KLS for more info about regularize least squares.</span><br><span class=help> See help of GREEDYKPCA for more info on approximation of data</span><br><span class=help> in the feature space.</span><br><span class=help> </span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> X [dim x num_data] Input column vectors.</span><br><span class=help> y [num_data x 1] Output values.</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 for more info.</span><br><span class=help> .arg [1 x narg] Kernel argument.</span><br><span class=help> .m [1x1] Maximal number of base vectors (Default m=0.25*num_data).</span><br><span class=help> .p [1x1] Depth of search for the best basis vector (Default p=m).</span><br><span class=help> .mserr [1x1] Desired mean squared reconstruction errors of approximation.</span><br><span class=help> .maxerr [1x1] Desired maximal reconstruction error of approximation.</span><br><span class=help> See 'help greedyappx' for more info about the stopping conditions.</span><br><span class=help> .verb [1x1] If 1 then some info is displayed (default 0).</span><br><span class=help> </span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Kernel projection:</span><br><span class=help> .Alpha [nsv x new_dim] Multipliers defining kernel projection.</span><br><span class=help> .sv.X [dim x num_data] Selected subset of the training vectors.</span><br><span class=help> .nsv [1x1] Number of basis vectors.</span><br><span class=help> .kercnt [1x1] Number of kernel evaluations.</span><br><span class=help> .MaxErr [1 x nsv] Maximal reconstruction error for corresponding</span><br><span class=help> number of base vectors.</span><br><span class=help> .MsErr [1 x nsv] Mean square reconstruction error for corresponding</span><br><span class=help> number of base vectors.</span><br><span class=help> </span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> x = [0:0.05:2*pi]; y = sin(x) + 0.1*randn(size(x));</span><br><span class=help> model = greedykls(x,y(:),struct('ker','rbf','arg',1,'lambda',0.001));</span><br><span class=help> y_est = kernelproj(x,model);</span><br><span class=help> figure; hold on;</span><br><span class=help> plot(x,y,'+k'); plot(x,y_est,'b'); </span><br><span class=help> plot(x,sin(x),'r'); plot(x(model.sv.inx),y(model.sv.inx),'ob');</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/kernelproj.html" target="mdsbody">KERNELPROJ</a>, <a href = "../kernels/extraction/kpca.html" target="mdsbody">KPCA</a>, <a href = "../kernels/extraction/greedykpca.html" target="mdsbody">GREEDYKPCA</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../kernels/list/greedykls.html">greedykls.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2005, 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> 01-mar-2005, VF<br> 22-feb-2005, VF<br></body></html>⌨️ 快捷键说明
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