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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">PBOUNDARY</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 given classifier in 2D.</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 = pboundary(model)</span><br><span class=help> h = pboundary(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 plots decision boundary of given classifier in </span><br><span class=help> 2-dimensional feature space. The classification function</span><br><span class=help> must be specified in the field model.eval. The decision</span><br><span class=help> bounary is interpolated from the response of the classifier</span><br><span class=help> y = feval( model.fun, X, model).</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 classifier.</span><br><span class=help> .fun [string] Classification function.</span><br><span class=help> </span><br><span class=help> options [struct] Controls visualization:</span><br><span class=help> .gridx [1x1] Sampling density in x-axis (default 200).</span><br><span class=help> .gridy [1x1] Sampling density in y-axis (default 200).</span><br><span class=help> .line_style [string] Used line-style to plot decision boundary.</span><br><span class=help> .fill [1x1] If 1 then the class regions are filled. </span><br><span class=help> </span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> h [1 x nobjects] Handles of used graphics 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> figure; </span><br><span class=help> ppatterns(data);</span><br><span class=help> pboundary( knnrule(data,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> <a href = "../visual/ppatterns.html" target="mdsbody">PPATTERNS</a>, <a href = "../visual/pline.html" target="mdsbody">PLINE</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../visual/list/pboundary.html">pboundary.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> 1-may-2004, VF<br> 19-may-2003, VF<br></body></html>
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