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Solving Selection Problems Using Preference Relation Based on Bayesian Learning

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Inductive Logic Programming (ILP 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1866))

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Abstract

This paper defines a selection problem which selects an appropriate object from a set that is specified by parameters. We discuss inductive learning of selection problems and give a method combining inductive logic programming (ILP) and Bayesian learning. It induces a binary relation comparing likelihood of objects being selected. Our methods estimate probability of each choice by evaluating variance of an induced relation from an ideal binary relation. Bayesian learning combines a prior probability of objects and the estimated probability. By making several assumptions on probability estimation, we give several methods. The methods are applied to Part-of-Speech tagging.

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Author information

Authors and Affiliations

  1. Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan

    Tomofumi Nakano & Nobuhiro Inuzuka

Authors
  1. Tomofumi Nakano
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  2. Nobuhiro Inuzuka
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of York, Heslington, York, YO10 5DD, UK

    James Cussens  & Alan Frisch  & 

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© 2000 Springer-Verlag Berlin Heidelberg

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Nakano, T., Inuzuka, N. (2000). Solving Selection Problems Using Preference Relation Based on Bayesian Learning. In: Cussens, J., Frisch, A. (eds) Inductive Logic Programming. ILP 2000. Lecture Notes in Computer Science(), vol 1866. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44960-4_9

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  • DOI: https://doi.org/10.1007/3-540-44960-4_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67795-6

  • Online ISBN: 978-3-540-44960-7

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