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Learnability of Probabilistic Automata via Oracles

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Algorithmic Learning Theory (ALT 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3734))

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Abstract

Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed μ-distinguishable. In this paper, we prove that state merging algorithms can be extended to efficiently learn a larger class of automata. In particular, we show learnability of a subclass which we call μ 2-distinguishable. Using an analog of the Myhill-Nerode theorem for probabilistic automata, we analyze μ-distinguishability and generalize it to μ p -distinguishability. By combining new results from property testing with the state merging algorithm we obtain KL-PAC learnability of the new automata class.

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Author information

Authors and Affiliations

  1. Statistical Machine Learning Program, National ICT Australia, RSISE, Australian National University, Canberra, ACT, Australia

    Omri Guttman, S. V. N. Vishwanathan & Robert C. Williamson

Authors
  1. Omri Guttman
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  2. S. V. N. Vishwanathan
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  3. Robert C. Williamson
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Editor information

Editors and Affiliations

  1. School of Computing, National University of Singapore, 117590, Singapore

    Sanjay Jain

  2. Ruhr-Universität Bochum, Germany

    Hans Ulrich Simon

  3. Department of Information and Communication Engineering, Faculty of Electro-Communications, The University of Electro-Communications, Chofugaoka 1–5–1, Chofu, 182-8585, Tokyo, Japan

    Etsuji Tomita

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© 2005 Springer-Verlag Berlin Heidelberg

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Guttman, O., Vishwanathan, S.V.N., Williamson, R.C. (2005). Learnability of Probabilistic Automata via Oracles. In: Jain, S., Simon, H.U., Tomita, E. (eds) Algorithmic Learning Theory. ALT 2005. Lecture Notes in Computer Science(), vol 3734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564089_15

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  • DOI: https://doi.org/10.1007/11564089_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29242-5

  • Online ISBN: 978-3-540-31696-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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