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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">PSVM</b><td valign="baseline" align="right" class="function"><a href="../visual/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Plots decision boundary of binary SVM classifier.</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> h = psvm(...)</span><br><span class=help> psvm(model)</span><br><span class=help> psvm(model,options)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function samples the Support Vector Machiones (SVM) decision </span><br><span class=help> function f(x) in 2D feature space and interpolates isoline </span><br><span class=help> width f(x)=0. The isolines f(x)=+1 and f(x)=-1 are plotted as well. </span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> model [struct] Model of binary SVM classifier:</span><br><span class=help> .Alpha [1 x nsv] Weights of training data.</span><br><span class=help> .b [real] Bias of decision function.</span><br><span class=help> .sv.X [dim x nsv] Support vectors.</span><br><span class=help> .options.ker [string] Kernel function identifier.</span><br><span class=help> See 'help kernel' for more info.</span><br><span class=help> .options.arg [1 x nargs] Kernel argument(s).</span><br><span class=help></span><br><span class=help> options [struct] Controls apperance:</span><br><span class=help> .background [1x1] If 1 then backgroud is colored according to </span><br><span class=help> the value of decision function (default 0).</span><br><span class=help> .sv [1x1] If 1 then the support vectors are marked (default 1).</span><br><span class=help> .sv_size [1x1] Marker size of the support vectors.</span><br><span class=help> .margin [1x1] If 1 then margin is displayed (default 1).</span><br><span class=help> .gridx [1x1] Sampling in x-axis (default 25).</span><br><span class=help> .gridy [1x1] Sampling in y-axis (default 25).</span><br><span class=help> .color [int] Color of decision boundary (default 'k').</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> h [struct] Handles of used graphical objects.</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 = smo( data, struct('ker','rbf','arg',1,'C',10) );</span><br><span class=help> figure; ppatterns(data); </span><br><span class=help> psvm( model, struct('background',1) );</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></span><br></code></div> <hr> <b>Source:</b> <a href= "../visual/list/psvm.html">psvm.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> 25-may-2004, VF<br> 10-may-2004, VF<br> 5-oct-2003, VF, returns handles<br> 14-Jan-2003, VF<br> 21-oct-2001, V.Franc<br> 16-april-2001, V. Franc, created<br></body></html>⌨️ 快捷键说明
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