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Identification in the Limit of Systematic-Noisy Languages

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Grammatical Inference: Algorithms and Applications (ICGI 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4201))

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Abstract

To study the problem of learning from noisy data, the common approach is to use a statistical model of noise. The influence of the noise is then considered according to pragmatic or statistical criteria, by using a paradigm taking into account a distribution of the data. In this article, we study the noise as a nonstatistical phenomenon, by defining the concept of systematic noise. We establish various ways of learning (in the limit) from noisy data. The first is based on a technique of reduction between problems and consists in learning from the data which one knows noisy, then in denoising the learned function. The second consists in denoising on the fly the training examples, thus to identify in the limit good examples, and then to learn from noncorrupted data. We give in both cases sufficient conditions so that learning is possible and we show through various examples (coming in particular from the field of the grammatical inference) that our techniques are complementary.

This work was supported in part by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002-506778. This publication only reflects the authors’views.

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Authors and Affiliations

  1. EURISE, Université de Saint-Etienne, 23 rue du Docteur Paul Michelon, 42023, Saint-Etienne

    Frédéric Tantini, Colin de la Higuera & Jean-Christophe Janodet

Authors
  1. Frédéric Tantini
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  2. Colin de la Higuera
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  3. Jean-Christophe Janodet
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Editor information

Editors and Affiliations

  1. Department of Biosciences and Informatics, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, 223-8522, Yokohama, Japan

    Yasubumi Sakakibara

  2. Dept. of Computer Science, Kyoto Sangyo University, Kamigamo Motoyama, Kita-ku, Kyoto, Japan

    Satoshi Kobayashi

  3. Japan Biological Informatics Consortium, 10F TIME24 Building, 2-45 Aomi, Koto-ku, 135-8073, Tokyo, Japan

    Kengo Sato

  4. Department of Information and Communication Engineering, Graduate School of Electro-Communications, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, 182-8585, Tokyo, Japan

    Tetsuro Nishino

  5. 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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© 2006 Springer-Verlag Berlin Heidelberg

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Tantini, F., de la Higuera, C., Janodet, JC. (2006). Identification in the Limit of Systematic-Noisy Languages. In: Sakakibara, Y., Kobayashi, S., Sato, K., Nishino, T., Tomita, E. (eds) Grammatical Inference: Algorithms and Applications. ICGI 2006. Lecture Notes in Computer Science(), vol 4201. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11872436_3

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  • DOI: https://doi.org/10.1007/11872436_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-45264-5

  • Online ISBN: 978-3-540-45265-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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