Document: autoclass-theory
Title: Bayesian Classification Theory
Author: Robin Hanson, John Stutz, and Peter Cheeseman
Abstract: The task of inferring a set of classes and class
 descriptions most likely to explain a given data set can be placed on
 a firm theoretical foundation using Bayesian statistics.  Within this
 framework, and using various mathematical and algorithmic
 approximations, the AutoClass system searches for the most probable
 classifications, automatically choosing the number of classes and
 complexity of class descriptions.  A simpler version of AutoClass
 has been applied to many large real data sets, have discovered new
 independently-verified phenomena, and have been released as a robust
 software package.  Recent extensions allow attributes to the
 selectively correlated within particular classes, and allow classes
 to inherit, or share, model parameters through a class hierarchy.  In
 this paper we summarize the mathematical foundations of Autoclass.
Section: Apps/Math

Format: PDF
Files: /usr/share/doc/autoclass/tr-fia-90-12-7-01.pdf
