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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">DEMO_SVM</b><td valign="baseline" align="right" class="function"><a href="../demos/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Demo on Support Vector Machines.</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> demo_svm</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> DEMO_SVM demonstrates algorithms training the binary </span><br><span class=help> SVM classifier L1-soft and L2-soft margin [<a href="../references.html#Vapnik95" title = "" >Vapnik95</a>]</span><br><span class=help> [<a href="../references.html#Cris00" title = "" >Cris00</a>]. The input training vectors must be 2-dimensional </span><br><span class=help> and can be interactively created by the user.</span><br><span class=help></span><br><span class=help> Following algorithms can be tested:</span><br><span class=help></span><br><span class=help> - Sequential Minimal Optimizer (SMO) for L1-norm soft margin.</span><br><span class=help> - QP solver (quadprog) used to train SVM with L2-norm soft margin.</span><br><span class=help> - Kernel Perceptron for separable hyperplane.</span><br><span class=help></span><br><span class=help> <span class=help_field>Control:</span></span><br><span class=help> Algorithm - algorithm for testing.</span><br><span class=help> Kernel - non-linear kernel.</span><br><span class=help> Kernel argument - argument of the non-linear kernel.</span><br><span class=help> C-constant - trade-off (regularization) constant.</span><br><span class=help> parameters - parameters of the selected algorithm.</span><br><span class=help> background - if selected then the background color</span><br><span class=help> denotes the sign and the intenzity denotes the value </span><br><span class=help> of the found decision function.</span><br><span class=help></span><br><span class=help> FIG2EPS - exports screen to the PostScript file.</span><br><span class=help> Load data - loads input training sets from file.</span><br><span class=help> Create data - calls program for creating point sets.</span><br><span class=help> Reset - clears the screen.</span><br><span class=help> Train SVM - trains and displays the SVM classifer.</span><br><span class=help> Info - calls the info box.</span><br><span class=help> Close - close the program.</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/smo.html" target="mdsbody">SMO</a>, <a href = "../svm/svmquadprog.html" target="mdsbody">SVMQUADPROG</a>, <a href = "../kernels/kperceptr.html" target="mdsbody">KPERCEPTR</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../demos/list/demo_svm.html">demo_svm.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> 2-june-2004, VF<br> 18-July-2003, VF<br> 6-march-2002, V.Franc<br> 23-oct-2001, V.Franc<br></body></html>⌨️ 快捷键说明
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