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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">LINCLASS</b><td valign="baseline" align="right" class="function"><a href="../linear/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Linear 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] = linclass( 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 classifies input data X using linear</span><br><span class=help> discriminant function:</span><br><span class=help></span><br><span class=help> y(i) = argmax W(:,y)'*X(:,i) + b(y)</span><br><span class=help> y</span><br><span class=help></span><br><span class=help> where parameters W [dim x nfun] and b [1 x nfun] are given </span><br><span class=help> in model and nfun is number of discriminant functions.</span><br><span class=help></span><br><span class=help> In the binary case (nfun=1) the classification rule is following</span><br><span class=help> y(i) = 1 if W'*X(:,i) + b >= 0</span><br><span class=help> 2 if W'*X(:,i) + b < 0</span><br><span class=help> </span><br><span class=help> where W [dim x 1], b [1x1] are parameters given in model.</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] Data to be classified.</span><br><span class=help></span><br><span class=help> model [struct] Parameters of linear classifier:</span><br><span class=help> .W [dim x nfun] Linear term.</span><br><span class=help> .b [nfun x 1] Bias.</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 [nfun x num_data] Values of discriminat function.</span><br><span class=help></span><br><span class=help> <span class=help_field>Examples:</span></span><br><span class=help> trn = load('riply_trn');</span><br><span class=help> tst = load('riply_tst');</span><br><span class=help> model = fld( trn );</span><br><span class=help> ypred = linclass( tst.X, model );</span><br><span class=help> cerror( ypred, tst.y )</span><br><span class=help> figure; ppatterns( trn ); pline( model );</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 = "../linear/finite/perceptron.html" target="mdsbody">PERCEPTRON</a>, <a href = "../linear/finite/mperceptron.html" target="mdsbody">MPERCEPTRON</a>, <a href = "../linear/fisher/fld.html" target="mdsbody">FLD</a>, ANDERSON.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../linear/list/linclass.html">linclass.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-may-2004, VF<br></body></html>⌨️ 快捷键说明
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