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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
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