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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">KFD</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 Fisher Discriminat.</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 = kfd( data )</span><br><span class=help> model = kfd( 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 Fisher</span><br><span class=help> Discriminant (KFD) [<a href="../references.html#Mika99a" title = "" >Mika99a</a>]. The aim is to find a binary </span><br><span class=help> kernel classifier which is the linear decision function in a </span><br><span class=help> feature space induced by the selected kernel function. </span><br><span class=help> The bias is found decision function is trainined by the </span><br><span class=help> linear SVM on the data projected on the optimal direction.</span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> data [struct] Training binary labeled 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(s).</span><br><span class=help> .C [1x1] Regularization constant of the linear 1-D SVM </span><br><span class=help> used to optimize the bias (default C=inf).</span><br><span class=help> .mu [1x1] Regularization constant added to the diagonal of </span><br><span class=help> the within scatter matrix (default 1e-4).</span><br><span class=help> </span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Binary SVM classifier:</span><br><span class=help> .Alpha [num_data x 1] Weight vector.</span><br><span class=help> .b [1x1] Bias of decision function.</span><br><span class=help> .sv.X [dim x num_data] Training data (support vectors).</span><br><span class=help></span><br><span class=help> .trnerr [1x1] Training classification error.</span><br><span class=help> .kercnt [1x1] Number of kernel evaluations used during training.</span><br><span class=help> .nsv [1x1] Number of support vectors.</span><br><span class=help> .options [struct] Copy of options.</span><br><span class=help> .cputime [1x1] Used cputime.</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> options = struct('ker','rbf','arg',1,'C',10,'mu',0.001);</span><br><span class=help> model = kfd(trn, options)</span><br><span class=help> figure; ppatterns(trn); psvm(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 = "../svm/svmclass.html" target="mdsbody">SVMCLASS</a>, <a href = "../linear/fisher/fld.html" target="mdsbody">FLD</a>, SVM.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../kernels/list/kfd.html">kfd.m</a> <p><b class="info_field">Modifications: </b> <br> 17-may-2004, VF<br> 14-may-2004, VF<br> 7-july-2003, VF<br></body></html>⌨️ 快捷键说明
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