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<!DOCTYPE HTML PUBLIC "-//IETF//DTD HTML//EN"><html><head><title>SOM Toolbox / lvq3 </title></head><body bgcolor=#f0f0f0><table border=0 width="100%" cellpadding=0 cellspacing=0><tr><td valign=baseline><font size=+2>SOM Toolbox</font></td><td valign=baseline align=center><a href="somtoolbox.html">Online documentation</td><td valign=baseline align=right><a href="http://www.cis.hut.fi/projects/somtoolbox/" target="_top">http://www.cis.hut.fi/projects/somtoolbox/</a></td></tr></table><hr><H1> lvq3 </H1><H3> Purpose </H3><PRE> Trains codebook with the LVQ3 -algorithm (described below).</PRE><H3> Syntax </H3><UL><PRE> sM = lvq3(sM, data, rlen, alpha, win, epsilon)</PRE></UL><H3> Description </H3><PRE> Trains codebook with the LVQ3 -algorithm. Codebook contains a number of vectors (mi, i=1,2,...,n) and so does data (vectors xj, j=1,2,...k). Both vector sets are classified: vectors may have a class (classes are set to data- or map -structure's 'labels' -field. For each xj the two closest codebookvectors mc1 and mc2 are searched (euclidean distances d1 and d2). xj must fall into the zone of window. That happens if: min(d1/d2, d2/d1) > s, where s = (1-win) / (1+win). If xj belongs to the same class of one of the mc1 and mc1, codebook is updated as follows (let mc1 belong to the same class as xj): mc1(t+1) = mc1(t) + alpha * (xj(t) - mc1(t)) mc2(t+1) = mc2(t) - alpha * (xj(t) - mc2(t)) If both mc1 and mc2 belong to the same class as xj, codebook is updated as follows: mc1(t+1) = mc1(t) + epsilon * alpha * (xj(t) - mc1(t)) mc2(t+1) = mc2(t) + epsilon * alpha * (xj(t) - mc2(t)) Otherwise updating is not performed. Argument 'rlen' tells how many times training -sequence is performed. Argument 'alpha' is recommended to be smaller than 0.1 and argument 'epsilon' should be between 0.1 and 0.5. NOTE: does not take mask into account.</PRE><H3> References </H3><PRE> Kohonen, T., "Self-Organizing Map", 2nd ed., Springer-Verlag, Berlin, 1995, pp. 181-182. See also LVQ_PAK from http://www.cis.hut.fi/research/som_lvq_pak.shtml</PRE><H3> Required input arguments </H3><PRE> sM The data to be trained. (struct) A map struct. data The data to use in training. (struct) A data struct. rlen (integer) Running length of LVQ3 -algorithm. alpha (float) Learning rate used in training, e.g. 0.05 win (float) Window length, e.g. 0.25 epsilon (float) Relative learning parameter, e.g. 0.3</PRE><H3> Output arguments </H3><PRE> sM Trained data. (struct) A map struct.</PRE><H3> Example </H3><PRE> lab = unique(sD.labels(:,1)); % different classes mu = length(lab)*5; % 5 prototypes for each sM = som_randinit(sD,'msize',[mu 1]); % initial prototypes sM.labels = [lab;lab;lab;lab;lab]; % their classes sM = lvq1(sM,sD,50*mu,0.05); % use LVQ1 to adjust % the prototypes sM = lvq3(sM,sD,50*mu,0.05,0.2,0.3); % then use LVQ3 </PRE><H3> See also </H3><TABLE NOBORDER WIDTH=80%><TR><TD><a href="lvq1.html"><B>lvq1</B></a><TD> Use LVQ1 algorithm for training.<TR><TD><a href="som_supervised.html"><B>som_supervised</B></a><TD> Train SOM using supervised training.<TR><TD><a href="som_seqtrain.html"><B>som_seqtrain</B></a><TD> Train SOM with sequential algorithm.</TABLE><p><hr><br><br><!-- Last updated: May 30 2002 --></body></html>⌨️ 快捷键说明
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