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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 3.2//EN"><!--Converted with LaTeX2HTML 97.1 (release) (July 13th, 1997) by Nikos Drakos (nikos@cbl.leeds.ac.uk), CBLU, University of Leeds* revised and updated by: Marcus Hennecke, Ross Moore, Herb Swan* with significant contributions from: Jens Lippman, Marek Rouchal, Martin Wilck and others --><HTML><HEAD><TITLE>1 Introduction</TITLE><META NAME="description" CONTENT="1 Introduction"><META NAME="keywords" CONTENT="DemoGNG"><META NAME="resource-type" CONTENT="document"><META NAME="distribution" CONTENT="global"><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=iso_8859_1"><LINK REL="STYLESHEET" HREF="DemoGNG.css"><LINK REL="next" HREF="node4.html"><LINK REL="previous" HREF="node2.html"><LINK REL="up" HREF="DemoGNG.html"><LINK REL="next" HREF="node4.html"></HEAD><BODY ><!--Navigation Panel--><A NAME="tex2html92" HREF="node4.html"><IMG WIDTH="37" HEIGHT="24" ALIGN="BOTTOM" BORDER="0" ALT="next" SRC="http://www.neuroinformatik.ruhr-uni-bochum.de/icons/next_motif.gif"></A> <A NAME="tex2html89" HREF="DemoGNG.html"><IMG WIDTH="26" HEIGHT="24" ALIGN="BOTTOM" BORDER="0" ALT="up" SRC="http://www.neuroinformatik.ruhr-uni-bochum.de/icons/up_motif.gif"></A> <A NAME="tex2html83" HREF="node2.html"><IMG WIDTH="63" HEIGHT="24" ALIGN="BOTTOM" BORDER="0" ALT="previous" SRC="http://www.neuroinformatik.ruhr-uni-bochum.de/icons/previous_motif.gif"></A> <A NAME="tex2html91" HREF="node1.html"><IMG WIDTH="65" HEIGHT="24" ALIGN="BOTTOM" BORDER="0" ALT="contents" SRC="http://www.neuroinformatik.ruhr-uni-bochum.de/icons/contents_motif.gif"></A> <BR><B> Next:</B> <A NAME="tex2html93" HREF="node4.html">2 System Requirements</A><B> Up:</B> <A NAME="tex2html90" HREF="DemoGNG.html">DemoGNG v1.5</A><B> Previous:</B> <A NAME="tex2html84" HREF="node2.html">List of Figures</A><BR><BR><!--End of Navigation Panel--><H1><A NAME="SECTION00030000000000000000">1 Introduction</A></H1><P>In the area of competitive learning a rather large number of models existwhich have similar goals but differ considerably in the way they work. Acommon goal of those algorithms is to distribute a certain number of vectorsin a possibly high-dimensional space. The distribution of these vectors shouldreflect (in one of several possible ways) the distribution of input signalswhich in general is not given explicitly but only through sample vectors.<P><BR><HR><ADDRESS><I>Hartmut S. Loos</I><BR><I>10/19/1998</I></ADDRESS></BODY></HTML>?? 快捷鍵說明
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