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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">KPERCEPTR</b><td valign="baseline" align="right" class="function"><a href="../kernels/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Kernel Perceptron.</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> model = kperceptr(data)</span><br><span class=help> model = kperceptr(data,options)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This function is an implementation of the kernel version</span><br><span class=help> of the Perceptron algorithm. The kernel perceptron search </span><br><span class=help> for the kernel binary classifier with zero emprical error.</span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> data [struct] Binary labeled training data:</span><br><span class=help> .X [dim x num_data] Vectors.</span><br><span class=help> .y [1 x num_data] Labels (1 or 2).</span><br><span class=help> </span><br><span class=help> options [struct] Control parameters:</span><br><span class=help> .ker [string] Kernel identifier (default 'linear').</span><br><span class=help> See 'help kernel' for more info.</span><br><span class=help> .arg [1 x nargs] Kernel argument.</span><br><span class=help> .tmax [1x1] Maximal number of iterations (default inf).</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Found kernel classifer:</span><br><span class=help> .Alpha [nsv x 1] Multipliers of the training data.</span><br><span class=help> .b [1x1] Bias of the decision rule.</span><br><span class=help> .sv.X [dim x nsv] Training data with non-zero Alphas.</span><br><span class=help> .exitflag [1x1] 1 ... Perceptron has converged.</span><br><span class=help> 0 ... Maximal number of iterations exceeded.</span><br><span class=help> .iter [1x1] Number of iterations.</span><br><span class=help> .kercnt [1x1] Number of kernel evaluations.</span><br><span class=help> .trnerr [1x1] Training classification error; Note: if exitflag==1 </span><br><span class=help> then trnerr = 0.</span><br><span class=help> .options [struct] Copy of options.</span><br><span class=help> .cputime [real] Used cputime in seconds.</span><br><span class=help> </span><br><span class=help> If the linear kernel is used then model.W [dim x 1] contains </span><br><span class=help> normal vector of the separating hyperplane.</span><br><span class=help> </span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> data = load('vltava');</span><br><span class=help> model = kperceptr(data, struct('ker','poly','arg',2));</span><br><span class=help> figure; ppatterns(data); pboundary(model);</span><br><span class=help> </span><br><span class=help> <span class=also_field>See also </span><span class=also><a href = "../svm/svmclass.html" target="mdsbody">SVMCLASS</a>, SVM.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../kernels/list/kperceptr.html">kperceptr.m</a> <p><b class="info_field">Modifications: </b> <br> 10-may-2004, VF<br> 18-July-2003, VF<br> 21-Nov-2001, V. Franc<br></body></html>⌨️ 快捷键说明
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