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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">MPERCEPTRON</b><td valign="baseline" align="right" class="function"><a href="../../linear/finite/index.html" target="mdsdir"><img border = 0 src="../../up.gif"></a></table> <p><b>Perceptron algorithm to train linear machine.</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 = mpeceptron(data)</span><br><span class=help> model = mpeceptron(data,options)</span><br><span class=help> model = mpeceptron(data,options,init_model)</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> model = mperceptron(data) uses the Perceptron learning rule</span><br><span class=help> to train linear machine (multi-class linear classitfier).</span><br><span class=help> The multi-class problem is transformed to the single-class</span><br><span class=help> one using the Kessler's construction [<a href="../../references.html#DHS01" title = "R.O.Duda, P.E.Hart, and D.G.Stork. Pattern Classification. John Wiley & Sons, 2nd. edition, 2001." >DHS01</a>][<a href="../../references.html#SH10" title = "M.I.Schlesinger and V.Hlavac. Ten lectures on statistical and structural pattern recognition. Kluwer Academic Publishers, 2002." >SH10</a>].</span><br><span class=help></span><br><span class=help> model = mperceptron(data,options) specifies stopping condition of</span><br><span class=help> the algorithm in structure options:</span><br><span class=help> .tmax [1x1]... maximal number of iterations.</span><br><span class=help></span><br><span class=help> model = mperceptron(data,options,init_model) specifies initial </span><br><span class=help> model which must contain:</span><br><span class=help> .W [dim x nclass] ... Normal vectors.</span><br><span class=help> .b [nclass x 1] ... Biases.</span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> data [struct] Labeled training data:</span><br><span class=help> .X [dim x num_data] Training vectors.</span><br><span class=help> .y [1 x num_data] Labels (1,2,...,nclass).</span><br><span class=help></span><br><span class=help> options [struct] </span><br><span class=help> .tmax [1x1] Maximal number of iterations (default tmax=inf).</span><br><span class=help> </span><br><span class=help> init_model [struct] Initial model; must contain items .W, .b.</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Multi-class linear classifier:</span><br><span class=help> .W [dim x nclass] Normal vectors.</span><br><span class=help> .b [nclass x 1] Biases.</span><br><span class=help></span><br><span class=help> .exitflag [1x1] 1 ... perceptron has converged.</span><br><span class=help> 0 ... number of iterations exceeded tmax.</span><br><span class=help> .t [1x1] Number of iterations.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> data = load('pentagon');</span><br><span class=help> model = mperceptron( data );</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></span><br><span class=help><span class=also> <a href = "../../linear/finite/perceptron.html" target="mdsbody">PERCEPTRON</a>, <a href = "../../linear/linclass.html" target="mdsbody">LINCLASS</a>, <a href = "../../linear/finite/ekozinec.html" target="mdsbody">EKOZINEC</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../linear/finite/list/mperceptron.html">mperceptron.m</a> <p><b class="info_field">Modifications: </b> <br> 21-may-2004, VF<br> 18-may-2004, VF<br></body></html>?? 快捷鍵說明
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