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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">BAYESCLS</b><td valign="baseline" align="right" class="function"><a href="../bayes/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Bayesian classifier with reject option.</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] = bayescls(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 classifier minimizing the Bayesian risk </span><br><span class=help> with 0/1-loss function. It corresponds to the minimization of </span><br><span class=help> probability of misclassification. The input vectors X are classified </span><br><span class=help> into classes with the highest a posterior probabilities computed from </span><br><span class=help> given model.</span><br><span class=help> </span><br><span class=help> The model contains parameters of conditional class probabilities</span><br><span class=help> in model.Pclass [cell 1 x num_classes] and a priory probabilities</span><br><span class=help> in model.Prior [1 x num_classes]. </span><br><span class=help></span><br><span class=help> The function</span><br><span class=help> p = feval(model.Pclass{i}.fun, X, model.pclass{i})</span><br><span class=help> is called to evaluate the i-the class conditional probability of X.</span><br><span class=help> </span><br><span class=help> It returns class labels y [1 x num_data] for each input vector</span><br><span class=help> and matrix dfce [num_class x num_data] of unnormalized a posterior</span><br><span class=help> probabilities</span><br><span class=help> dfce(y,i) = Conditional_probability(X(:,i)|y)*Prior(y).</span><br><span class=help></span><br><span class=help> If the field model.eps exists then the Bayesian classifier </span><br><span class=help> with the reject option is used. The eps is penalty for the </span><br><span class=help> decision "don't know" which is indicated by label y = 0.</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></span><br><span class=help> model [struct] Describes probabilistic model:</span><br><span class=help> .Pclass [cell 1 x num_classes] Class conditional probabilities.</span><br><span class=help> .Prior [1 x num_classes] A priory probabilities.</span><br><span class=help></span><br><span class=help> .eps [1x1] (optional) Penalty of decision "don't know". </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] Labels (1 to num_classes); 0 for "don't know".</span><br><span class=help> dfce [num_classes x num_data] Unnormalized a posterior </span><br><span class=help> probabilities (see above).</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> trn = load('riply_trn');</span><br><span class=help> tst = load('riply_tst');</span><br><span class=help> inx1 = find(trn.y==1);</span><br><span class=help> inx2 = find(trn.y==2);</span><br><span class=help> model.Pclass{1} = mlcgmm(trn.X(:,inx1));</span><br><span class=help> model.Pclass{2} = mlcgmm(trn.X(:,inx2));</span><br><span class=help> model.Prior = [length(inx1) length(inx2)]/(length(inx1)+length(inx2));</span><br><span class=help> ypred = bayescls(tst.X,model);</span><br><span class=help> cerror(ypred,tst.y)</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 = "../bayes/bayesdf.html" target="mdsbody">BAYESDF</a>, <a href = "../bayes/bayeserr.html" target="mdsbody">BAYESERR</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../bayes/list/bayescls.html">bayescls.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> 09-jun-2004, VF<br> 01-may-2004, VF<br> 11-mar-2004, VF, "don't" know decision added.<br> 19-sep-2003, VF<br></body></html>?? 快捷鍵說明
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