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International Conference on Algorithmic Learning Theory

ALT 1999: Algorithmic Learning Theory pp 231–240Cite as

Learning Real Polynomials with a Turing Machine

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1720))

Abstract

We provide an algorithm to PAC learn multivariate polynomials with real coefficients. The instance space from which labeled samples are drawn is IRN but the coordinates of such samples are known only approximately. The algorithm is iterative and the main ingredient of its complexity, the number of iterations it performs, is estimated using the condition number of a linear programming problem associated to the sample. To the best of our knowledge, this is the first study of PAC learning concepts parameterized by real numbers from approximate data.

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Author information

Authors and Affiliations

  1. Department of Mathematics, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon, Hong Kong

    Dennis Cheung

Authors
  1. Dennis Cheung
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Editor information

Editors and Affiliations

  1. Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Tokyo, 152-8552, Japan

    Osamu Watanabe

  2. Waseda University, 1-6-1 Nishiwaseda, Shinjuku-ku, Tokyo, 169-8050, Japan

    Takashi Yokomori

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© 1999 Springer-Verlag Berlin Heidelberg

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Cheung, D. (1999). Learning Real Polynomials with a Turing Machine. In: Watanabe, O., Yokomori, T. (eds) Algorithmic Learning Theory. ALT 1999. Lecture Notes in Computer Science(), vol 1720. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46769-6_19

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  • DOI: https://doi.org/10.1007/3-540-46769-6_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66748-3

  • Online ISBN: 978-3-540-46769-4

  • eBook Packages: Springer Book Archive

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