?? perceptron.html
字號:
<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">PERCEPTRON</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 binary 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> model = perceptron(data)</span><br><span class=help> model = perceptron(data,options)</span><br><span class=help> model = perceptron(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 = perceptron(data) uses the Perceptron learning rule</span><br><span class=help> to find separating hyperplane from given binary labeled data.</span><br><span class=help></span><br><span class=help> model = perceptron(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> If tmax==-1 then it only returns index (model.last_update)</span><br><span class=help> of data vector which should be used by the algorithm for updating</span><br><span class=help> the linear rule in the next iteration.</span><br><span class=help></span><br><span class=help> model = perceptron(data,options,init_model) specifies initial model</span><br><span class=help> which must contain:</span><br><span class=help> .W [dim x 1] ... normal vector.</span><br><span class=help> .b [1x1] ... bias of hyperplane.</span><br><span class=help> .t [1x1] (optional) ... iteration number.</span><br><span class=help></span><br><span class=help> <span class=help_field>Input:</span></span><br><span class=help> data [struct] Labeled (binary) training data. </span><br><span class=help> .X [dim x num_data] Input 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] </span><br><span class=help> .tmax [1x1] Maximal number of iterations (default tmax=inf).</span><br><span class=help> If tmax==-1 then it does not perform any iteration but returns only </span><br><span class=help> index of the point which should be used to update linear rule.</span><br><span class=help> </span><br><span class=help> init_model [struct] Initial model; must contain items</span><br><span class=help> .W, .b and .t (see above).</span><br><span class=help></span><br><span class=help> <span class=help_field>Output:</span></span><br><span class=help> model [struct] Binary linear classifier:</span><br><span class=help> .W [dim x 1] Normal vector of hyperplane.</span><br><span class=help> .b [1x1] Bias of hyperplane.</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 [int] Number of iterations.</span><br><span class=help> .last_update [d x 1] Index of the last point used for update.</span><br><span class=help></span><br><span class=help> <span class=help_field>Example:</span></span><br><span class=help> data = genlsdata( 2, 50, 1);</span><br><span class=help> model = perceptron(data)</span><br><span class=help> figure; ppatterns(data); 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/ekozinec.html" target="mdsbody">EKOZINEC</a>, <a href = "../../linear/finite/mperceptron.html" target="mdsbody">MPERCEPTRON</a>, <a href = "../../linear/linclass.html" target="mdsbody">LINCLASS</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../../linear/finite/list/perceptron.html">perceptron.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> 17-sep-2003, VF<br> 16-Feb-2003, VF<br> 20-Jan-2003, VF<br> 7-jan-2002, VF. A new coat.<br> 24. 6.00 V. Hlavac, comments polished.<br> 15-dec-2000, texts, returns bad point<br></body></html>?? 快捷鍵說明
復制代碼
Ctrl + C
搜索代碼
Ctrl + F
全屏模式
F11
切換主題
Ctrl + Shift + D
顯示快捷鍵
?
增大字號
Ctrl + =
減小字號
Ctrl + -