A COVERING ALGORITHM IN MACHINE LEARNING FROM THE POINT OF VIEW OF THE SET THEORY Academic Article uri icon

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abstract

  • The empirical inductive algorithms that utilize the covering paradigm (such as the AQ x and CN x families of inductive systems) comprise various heuristics and statistical tools so that the core of the covering paradigm remains often quite hidden. The goal of this paper is thus to disclose theoretical underlying principles of covering learning algorithms. By exploiting the set theory, the paper exhibits how the correctness and generality required for decision rules induced by a covering algorithm may be satisfied. The principle differences between a genuine theoretical approach and actual empirical machine learning algorithms are also discussed.

publication date

  • May 1996