Compact coding of automata for inference

Matthew Collins, Ingrid Zukerman

Research output: Contribution to journalConference articleResearchpeer-review

Abstract

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
Pages (from-to)395-400
Number of pages6
JournalNational Conference Publication - Institution of Engineers, Australia
Volume1
Issue number94 /9
Publication statusPublished - 1 Dec 1994
EventProceedings of the International Symposium on Information Theory & Its Applications 1994. Part 1 (of 2) - Sydney, Aust
Duration: 20 Nov 199424 Nov 1994

Cite this

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Compact coding of automata for inference. / Collins, Matthew; Zukerman, Ingrid.

In: National Conference Publication - Institution of Engineers, Australia, Vol. 1, No. 94 /9, 01.12.1994, p. 395-400.

Research output: Contribution to journalConference articleResearchpeer-review

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AU - Zukerman, Ingrid

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N2 - 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.

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