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Function Decomposition in Machine Learning

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Machine Learning and Its Applications (ACAI 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2049))

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Abstract

To solve a complex problem, one of the effective general approaches is to decompose it into smaller, less complex and more manageable subproblems. In machine learning, this principle is a foundation for structured induction [44]: instead of learning a single complex classification rule from examples, define a concept hierarchy and learn rules for each of the (sub)concepts. Shapiro [44] used structured induction for the classification of a fairly complex chess endgame and demonstrated that the complexity and comprehensiveness (“brain-compatibility”) of the obtained solution was superior to the unstructured one. Shapiro was helped by a chess master to structure his problem domain. Typically, applications of structured induction involve a manual development of the hierarchy and a manual selection and classification of examples to induce the subconcept classification rules; usually this is a tiresome process that requires an active availability of a domain expert over long periods of time. Therefore, it would be very desirable to automate the problem decomposition task.

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Author information

Authors and Affiliations

  1. Faculty of Computer and Information Sciences, Slovenia

    Blaž Zupan, Ivan Bratko & Janez Demšar

  2. Department of Intelligent Systems, J. Stefan Institute, Jamova 39, SI-1000, Ljubljana, Slovenia

    Blaž Zupan, Ivan Bratko & Marko Bohanec

  3. Baylor College of Medicine, 1 Baylor Plaza, Houston, TX, 77030, USA

    Blaž Zupan

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  1. Blaž Zupan
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  2. Ivan Bratko
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  4. Janez Demšar
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  1. National Centre for Scientific Research “Demokritos”, Institute of Informatics and Telecommunications, P.O. Box 60228, Ag. Paraskevi, 15310, Athens, Greece

    Georgios Paliouras , Vangelis Karkaletsis  & Constantine D. Spyropoulos ,  & 

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Zupan, B., Bratko, I., Bohanec, M., Demšar, J. (2001). Function Decomposition in Machine Learning. In: Paliouras, G., Karkaletsis, V., Spyropoulos, C.D. (eds) Machine Learning and Its Applications. ACAI 1999. Lecture Notes in Computer Science(), vol 2049. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44673-7_4

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  • DOI: https://doi.org/10.1007/3-540-44673-7_4

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