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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">DEMO_ANDERSON</b><td valign="baseline" align="right" class="function"><a href="../demos/index.html" target="mdsdir"><img border = 0 src="../up.gif"></a></table> <p><b>Demo on Generalized Anderson's task.</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> demo_anderson</span><br><span class=help></span><br><span class=help> <span class=help_field>Description:</span></span><br><span class=help> This demo demonstrates the algorithms which solve </span><br><span class=help> the Generalized Anderson`s Task (GAT) [<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>]. The GAT is an </span><br><span class=help> instance of the non-Bayesian task of decision under </span><br><span class=help> non-random intervention. </span><br><span class=help> </span><br><span class=help> The goal of is to find a binary linear classification</span><br><span class=help> rule (g(x)=sgn(W'*x+b) (line in 2D) with minimal probability of</span><br><span class=help> misclassification. The conditional probabilities are known to</span><br><span class=help> be Gaussians their paramaters belong to a given set of </span><br><span class=help> parameters. The true parameters are not known. The linear rule </span><br><span class=help> which guarantes the minimimal classification error for the worst</span><br><span class=help> possible case (the worst configuration of Gaussains) is</span><br><span class=help> sought for.</span><br><span class=help> </span><br><span class=help> The found solution (hyperplane, line in 2D) is vizualized </span><br><span class=help> as well as the input Gaussians which describe input classes.</span><br><span class=help></span><br><span class=help> Following algorithms can be tested:</span><br><span class=help> </span><br><span class=help> Eps-solution - Finds epsilon-solution of the GAT in finite number</span><br><span class=help> of iterations if such solution exist. The epsilon means</span><br><span class=help> desired classification error.</span><br><span class=help> Original - Original Anderson-Bahadur's algorithm defined for </span><br><span class=help> two Gaussians only (each class one Gaussian).</span><br><span class=help> Optimal - Implementation of general algorithm propsed by Schlesinger.</span><br><span class=help> It finds the optimal solution.</span><br><span class=help> Gradient - Fast and simple implementation which uses the generalized</span><br><span class=help> gradient descent optimization.</span><br><span class=help></span><br><span class=help> <span class=help_field>Control:</span></span><br><span class=help> Algorithm - select algorithm for testing.</span><br><span class=help> Parameter - parameters for the selected algorithm.</span><br><span class=help> Iterations - number of iterations in one step.</span><br><span class=help> Animation - enable/dissable animation.</span><br><span class=help></span><br><span class=help> FIG2EPS - export screen to the PostScript file.</span><br><span class=help> Load data - load input point sets from file.</span><br><span class=help> Create data - call interactive program for creating sets of Gaussians.</span><br><span class=help> Reset - set the tested algorithm to the initial state.</span><br><span class=help> Play - run the tested algorithm.</span><br><span class=help> Stop - stop the running algorithm.</span><br><span class=help> Step - perform only one step.</span><br><span class=help> Info - display the info box.</span><br><span class=help> Close - close the program.</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/anderson/eanders.html" target="mdsbody">EANDERS</a>, <a href = "../linear/anderson/androrig.html" target="mdsbody">ANDRORIG</a>, <a href = "../linear/anderson/ggradandr.html" target="mdsbody">GGRADANDR</a>, <a href = "../linear/anderson/ganders.html" target="mdsbody">GANDERS</a>.</span><br><span class=help></span><br></code></div> <hr> <b>Source:</b> <a href= "../demos/list/demo_anderson.html">demo_anderson.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> 11-June-2001, V.Franc, comments added.<br> 24. 6.00 V. Hlavac, comments polished.<br></body></html>?? 快捷鍵說明
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