Feb 1, 2023 · In this paper we present a quantitative measure for evaluating the effect of small disjuncts on learning, and use it to analyze thirty benchmark ...
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A Quantitative Study of Small Disjuncts. Gary M. Weiss. ∗ and Haym Hirsh. Department of Computer Science. Rutgers University. New Brunswick, New Jersey 08903.
In this paper we present a quantitative measure for evaluating the effect of small disjuncts on learning and use it to analyze 30 benchmark datasets. We ...
A Quantitative Study of Small Disjuncts. Gary M. Weiss. ∗ and Haym Hirsh. Department of Computer Science. Rutgers University. New Brunswick, New Jersey 08903.
This paper presents a quantitative measure for evaluating the effect of small disjuncts on learning and uses it to analyze 30 benchmark datasets and comes ...
Abstract. Classifier systems that learn from examples often express the learned concept in the form of a disjunctive description. Disjuncts that correctly ...
In this paper we present a quantitative measure for evaluating the effect of small disjuncts on learning and use it to analyze 30 benchmark datasets. We ...
This article provides a much more systematic study of small disjuncts and analyzes how they affect classifiers induced from thirty real-world data sets to ...
Jul 30, 2000 · A Quantitative Study of Small Disjuncts. Authors: Author Picture Gary ... A Quantitative Study of Small Disjuncts. Pages 665–670. Previous ...
A Quantitative Study of Small Disjuncts. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, Austin, Texas. (pdf). An expanded ...