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A Theory and Methodology of Inductive Learning

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Machine Learning

Part of the book series: Symbolic Computation ((1064))

Abstract

The presented theory views inductive learning as a heuristic search through a space of symbolic descriptions, generated by an application of various inference rules to the initial observational statements. The inference rules include generalization rules, which perform generalizing transformations on descriptions, and conventional truth-preserving deductive rules. The application of the inference rules to descriptions is constrained by problem background knowledge, and guided by criteria evaluating the “quality” of generated inductive assertions.

Based on this theory, a general methodology for learning structural descriptions from examples, called Star, is described and illustrated by a problem from the area of conceptual data analysis.

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Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, USA

    Ryszard S. Michalski

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  1. Ryszard S. Michalski
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Editors and Affiliations

  1. University of Illinois at Urbana-Champaign, IL, USA

    Ryszard S. Michalski (Professor of Computer Science and Medical Information Science, and the Director of the Artificial Intelligence Laboratory) (Professor of Computer Science and Medical Information Science, and the Director of the Artificial Intelligence Laboratory)

  2. Carnegie-Mellon University Pittsburgh, PA, USA

    Jaime G. Carbonell (Associate Professor in Computer Science) (Associate Professor in Computer Science)

  3. Rutgers University New Brunswick, NJ, USA

    Tom M. Mitchell (Associate Professor of Computer Science) (Associate Professor of Computer Science)

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Michalski, R.S. (1983). A Theory and Methodology of Inductive Learning. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds) Machine Learning. Symbolic Computation. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-12405-5_4

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  • DOI: https://doi.org/10.1007/978-3-662-12405-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-12407-9

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