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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN"><!--Converted with LaTeX2HTML 2002-2-1 (1.70)original version by: Nikos Drakos, CBLU, University of Leeds* revised and updated by: Marcus Hennecke, Ross Moore, Herb Swan* with significant contributions from: Jens Lippmann, Marek Rouchal, Martin Wilck and others --><HTML><HEAD><TITLE>Tutorial: Gaussian Statistics and Unsupervised Learning</TITLE><META NAME="description" CONTENT="Tutorial: Gaussian Statistics and Unsupervised Learning"><META NAME="keywords" CONTENT="Gaussian"><META NAME="resource-type" CONTENT="document"><META NAME="distribution" CONTENT="global"><META NAME="Generator" CONTENT="LaTeX2HTML v2002-2-1"><META HTTP-EQUIV="Content-Style-Type" CONTENT="text/css"><LINK REL="STYLESHEET" HREF="../../ci.css"><LINK REL="next" HREF="node1.html"></HEAD><BODY bgcolor="#ffffff"><DIV CLASS="navigation"><table border=0 cellspacing=0 callpadding=0 width=100% class="tut_nav"><tr valign=middle class="tut_nav"><td valign=middle align=left class="tut_nav"><i><b> </b></i></td><td valign=middle align=right class="tut_nav"> <a href="index.html"><img ALIGN="absmiddle" BORDER="0" ALT="Contents" src="contents.gif"></a> <A NAME="tex2html7" HREF="node1.html"><IMG ALIGN="absmiddle" BORDER="0" ALT="next" SRC="next.gif"></A></dt></tr></table></DIV><!--End of Navigation Panel--><P><P><h1>Gaussian Statistics and Unsupervised Learning</h1>A Tutorial for the Course <em>Computational Intelligence</em><P><P><H3><A NAME="SECTION00000100000000000000">Abstract</A></H3>
This tutorial presents the properties of the Gaussian probability
density function. Subsequently, supervised and unsupervised pattern
recognition methods are treated. Supervised classification algorithms
are based on a labeled data set. The knowledge about the class
membership of this training data set is used for the classification of
new samples. Unsupervised learning methods establish clusters from an
unlabeled training data set. Clustering algorithms such as the
<SPAN CLASS="MATH"><IMG WIDTH="16" HEIGHT="14" ALIGN="BOTTOM" BORDER="0" SRC="img4.gif" ALT="$ K$"></SPAN>-means, the EM (expectation-maximization) algorithm, and the
Viterbi-EM algorithm are presented.<P><H3><A NAME="SECTION00000200000000000000">Usage</A></H3> To make full use of this tutorial you have to
<OL><LI>download the file
<A NAME="tex2html1" HREF="http://www.igi.tugraz.at/lehre/CI/tutorials/Gaussian.zip"><TT>Gaussian.zip</TT></A>which contains this tutorial in
printable format (<A NAME="tex2html2" HREF="Gaussian.pdf">PDF</A>and
<A NAME="tex2html3" HREF="Gaussian.ps.gz">ps.gz</A>) and the accompanying Matlab
programs.<P></LI><LI>Unzip <TT>Gaussian.zip</TT> which will generate a
subdirectory named <TT>Gaussian/matlab</TT> where you can find
all the Matlab programs.<P></LI><LI>Add the path <TT>Gaussian/matlab</TT> to the matlab search path,
for example with a command like
<code>addpath('C:\Work\Gaussian\matlab')</code> if you are using a Windows
machine, or by using a command like
<code>addpath('/home/jack/Gaussian/matlab')</code> if you are on a
Unix/Linux machine.<P></LI></OL><P><H3><A NAME="SECTION00000300000000000000">Sources</A></H3>
This tutorial is based on <P><UL><LI>EPFL lab notes
``Introduction to Gaussian Statistics and Statistical Pattern
Recognition'' by Hervé Bourlard, Sacha Krstulovic, and Mathew
Magimai-Doss.
</LI></UL><P><H3><A NAME="SECTION00000400000000000000">Contents</A></H3><P><!--Table of Child-Links--><A NAME="CHILD_LINKS"></A><UL CLASS="ChildLinks"><LI><A NAME="tex2html9" HREF="node1.html">Gaussian statistics</A><LI><A NAME="tex2html10" HREF="node2.html">Statistical pattern recognition</A><LI><A NAME="tex2html11" HREF="node3.html">Unsupervised training</A></UL><!--End of Table of Child-Links--><DIV CLASS="navigation"><br><table border=0 cellspacing=0 callpadding=0 width=100% class="tut_nav"><tr valign=middle class="tut_nav"><td align=right valign=middle class="tut_nav"><A NAME="tex2html8" HREF="node1.html">Gaussian statistics</A> <A NAME="tex2html7" HREF="node1.html"><IMG ALIGN="absmiddle" BORDER="0" ALT="next" SRC="next.gif"></A></td></tr></table></DIV><!--End of Navigation Panel--></BODY></HTML>⌨️ 快捷键说明
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