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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">SVMLIGHT</b><td valign="baseline" align="right" class="function"><a href="../svm/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Interface to SVM^{light} software.</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 = svmlight(data)</span><br><span class=help> model = svmlight(data,options)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function serves as an interface between Matlab </span><br><span class=help> and SVM^{light} (Version: 5.00) optimizer which trains </span><br><span class=help> the Support Vector Machines classifier.</span><br><span class=help></span><br><span class=help> The executable file 'svm_learn' must be in the path. </span><br><span class=help> The SVM^{light} software can be downloaded from:</span><br><span class=help> http://svmlight.joachims.org/</span><br><span class=help></span><br><span class=help> This function creates temporary files 'tmp_alphaXX.txt', </span><br><span class=help> 'tmp_examplesXX.txt', 'tmp_modelXX.txt' and 'tmp_verbXX.txt' for </span><br><span class=help> comunication with the SVM^{light} software. The XX=datestr(now)</span><br><span class=help> is string consisting of current date and time.</span><br><span class=help> </span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> data [struct] Labeled binary data:</span><br><span class=help> .X [dim x num_data] Training vectors.</span><br><span class=help> .y [1 x num_data] Labels of training data (1 or 2).</span><br><span class=help> </span><br><span class=help> options [struct] Control parameters:</span><br><span class=help> .ker [string] Kernel identifier: </span><br><span class=help> 'linear' (default),'rbf' and 'poly'. </span><br><span class=help> .arg [1x1] Kernel argument (default []).</span><br><span class=help> .C [1x1] SVM regularization constant (default C=inf).</span><br><span class=help> .j [1x1] Cost-factor, by which training errors on </span><br><span class=help> positive examples outweight errors on negative examples (default 1).</span><br><span class=help> .eps [1x1] Tolerance of KKT-conditions (default eps=0.001).</span><br><span class=help> .keep_files [1x1] If ==1 then keeps temporary files otherwise</span><br><span class=help> erase them.</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Binary SVM classifier:</span><br><span class=help> .Alpha [nsv x 1] Weights of support vectors.</span><br><span class=help> .b [1x1] Bias of decision function.</span><br><span class=help> .sv.X [dim x nsv] Support vectors.</span><br><span class=help> .sv.inx [1 x nsv] Indices of SVs (model.sv.X = data.X(:,inx)).</span><br><span class=help> .nsv [int] Number of Support Vectors.</span><br><span class=help> .kercnt [int] Number of kernel evaluations used by the SVM^{light}.</span><br><span class=help> .trnerr [real] Classification error on training data.</span><br><span class=help> .margin [real] Margin of found classifier.</span><br><span class=help> .options [struct] Copy of used options.</span><br><span class=help> .cputime [real] Used CPU time in seconds.</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> model=svmlight(data,struct('ker','rbf','C',10,'arg',1))</span><br><span class=help> figure; ppatterns(data); psvm(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 = "../svm/svmclass.html" target="mdsbody">SVMCLASS</a>, <a href = "../svm/xy2svmlight.html" target="mdsbody">XY2SVMLIGHT</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../svm/list/svmlight.html">svmlight.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2003, 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> 16-may-2004, VF<br> 15-jan-2004, VF, handling argument of poly kernel repared<br> 10-oct-2003, VF, computation of lin model added<br> 29-aug-2003, VF, seconds are added to the name of temporary files<br> 12-may-2003, VF, 1st 3 lines of verb_file are skiped<br> 31-jan-2003, VF, added option 'j' <br> 28-Jan-2003, VF<br> 20-jan-2003, VF, temporary files are unique and are deleted at the end<br> 14-Jan-2003, VF<br> 26-sep-2002, VF<br> 3-Jun-2002, V.Franc<br></body></html>⌨️ 快捷键说明
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