Concept learning by example decomposition

    Authors

    S. Joshi

    Comments

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    Abbreviated Journal Title

    J. Exp. Theor. Artif. Intell.

    Keywords

    concept learning; example decomposition; concept decomposition; probably; approximately correct; computational learning theory; learning by; decomposition; FEATURE-EXTRACTION; MODEL; Computer Science, Artificial Intelligence

    Abstract

    It is widely accepted in machine learning that it is easier to learn several smaller decomposed concepts than a single large one. Typically, such decomposition of concepts is achieved in highly constrained environments, or aided by human experts. In this article, we investigate concept learning by example decomposition in a general probably approximately correct setting for Boolean learning. We develop sample complexity bounds for the different steps involved in the process. We formally show that if the cost of example partitioning is kept low then it is highly advantageous to learn by example decomposition.

    Journal Title

    Journal of Experimental & Theoretical Artificial Intelligence

    Volume

    22

    Issue/Number

    1

    Publication Date

    1-1-2010

    Document Type

    Article

    Language

    English

    First Page

    1

    Last Page

    21

    WOS Identifier

    WOS:000274673700001

    ISSN

    0952-813X

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