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Learning Comprehensible Theories from Structured Data

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Advanced Lectures on Machine Learning

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2600))

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Abstract

This tutorial discusses some knowledge representation issues in machine learning. The focus is on machine learning applications for which the individuals that are the subject of learning have complex structure. To represent such individuals,a rich knowledge representation language based on higher-order logic is introduced. The logic is also employed to construct comprehensible hypotheses that one might want to learn about the individuals. The tutorial introduces the main ideas of this approach to knowledge representation in a mostly informal way and gives a number of illustrations. The application of the ideas to decision-tree learning is also illustrated with an example.

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References

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Author information

Authors and Affiliations

  1. Research School of Information Sciences and Engineering, The Australian National University, 0200, Canberra, ACT, Australia

    J.W. Lloyd

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  1. J.W. Lloyd
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Editor information

Editors and Affiliations

  1. RSISE, The Australian National University, 0200, Canberra, ACT, Australia

    Shahar Mendelson

  2. Research School for Information Sciences and Engineering, The Australian National University, 0200, Canberra, ACT, Australia

    Alexander J. Smola

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© 2003 Springer-Verlag Berlin Heidelberg

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Lloyd, J. (2003). Learning Comprehensible Theories from Structured Data. In: Mendelson, S., Smola, A.J. (eds) Advanced Lectures on Machine Learning. Lecture Notes in Computer Science(), vol 2600. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36434-X_6

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  • DOI: https://doi.org/10.1007/3-540-36434-X_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00529-2

  • Online ISBN: 978-3-540-36434-4

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