Inferring probabilistic acyclic automata using the minimum description length principle

Yoram Singer, Naftali Tishby

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The use of Rissanen's minimum description length principle for the construction of probabilistic acyclic automata (PAA) is explored. We propose a learning algorithm for a PAA that is adaptive both in the structure and the dimension of the model. The proposed algorithm was tested on synthetic data as well as on real pattern recognition problems.

Original languageEnglish (US)
Title of host publicationIEEE International Symposium on Information Theory - Proceedings
PublisherIEEE
StatePublished - Dec 1 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Symposium on Information Theory - Trodheim, Norw
Duration: Jun 27 1994Jul 1 1994

Other

OtherProceedings of the 1994 IEEE International Symposium on Information Theory
CityTrodheim, Norw
Period6/27/947/1/94

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Inferring probabilistic acyclic automata using the minimum description length principle'. Together they form a unique fingerprint.

  • Cite this

    Singer, Y., & Tishby, N. (1994). Inferring probabilistic acyclic automata using the minimum description length principle. In IEEE International Symposium on Information Theory - Proceedings IEEE.