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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">ADACLASS</b><td valign="baseline" align="right" class="function"><a href="../misc/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>AdaBoost 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> [y,dfce] = adaclass(X,model)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function implements the AdaBoost classifier which</span><br><span class=help> its discriminant function is composed of a weighted sum</span><br><span class=help> of binary rules. It is assumed here that the binary rules</span><br><span class=help> respond with label 1 or 2 (not 1 and -1 as used in </span><br><span class=help> AdaBoost literature).</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] Vectors to be classified.</span><br><span class=help> model [struct] AdaBoost classifier:</span><br><span class=help> .rule [cell 1 x T] Binary weak rules.</span><br><span class=help> .Alpha [1 x T] Weights of the weak rules.</span><br><span class=help> .fun = 'adaclass' (optinal).</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> y [1 x num_data] Predicted labels.</span><br><span class=help> dfce [1 x num_data] Values of weighted sum of </span><br><span class=help> weak rules; y(i) = 1 if dfce(i) >= 0 and</span><br><span class=help> y(i) = 2 if dfce(i) < 0.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> trn_data = load('riply_trn');</span><br><span class=help> tst_data = load('riply_tst');</span><br><span class=help> options.learner = 'weaklearner';</span><br><span class=help> options.max_rules = 50;</span><br><span class=help> options.verb = 1;</span><br><span class=help> model = adaboost(trn_data, options);</span><br><span class=help> ypred1 = adaclass(trn_data.X,model);</span><br><span class=help> ypred2 = adaclass(tst_data.X,model);</span><br><span class=help> trn_err = cerror(ypred1,trn_data.y)</span><br><span class=help> tst_err = cerror(ypred2,tst_data.y)</span><br><span class=help></span><br><span class=help> See also: </span><br><span class=help> ADABOOST, WEAKLEARNER.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../misc/list/adaclass.html">adaclass.m</a> <p><b class="info_field">About: </b> Statistical Pattern Recognition Toolbox<br> (C) 1999-2004, 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-aug-2004, VF<br> 11-aug-2004, VF<br></body></html>⌨️ 快捷键说明
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