Compact coding of automata for inference

Matthew Collins, Ingrid Zukerman

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In this paper we present an information theoretic measure for the unsupervised inference of Probabilistic Recursive Transition Networks and Probabilistic Finite State Automata from bodies of training data. The method we present here is based on the Minimum Message Length principle. It constructs a compact code or message which contains a trial theory (automaton) about the data followed by the data encoded using the theory. The trial theory with the shortest message length (including the data) contains the most likely explanation theory about the data among all the trial theories. We do not present an algorithm for generating the trial theories as it may vary depending on the domain of the problem.

Original languageEnglish
Title of host publicationProceedings of the International Symposium on Information Theory & Its Applications 1994
Number of pages6
Edition94 /9
Publication statusPublished - 1 Dec 1994
EventInternational Symposium on Information Theory and Its Applications 1994 - Sydney, Australia
Duration: 20 Nov 199424 Nov 1994

Publication series

NameNational Conference Publication - Institution of Engineers, Australia
ISSN (Print)0313-6922


ConferenceInternational Symposium on Information Theory and Its Applications 1994

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