Computational Limitations on Learning from Examples (1988)
| Venue: | Journal of the ACM |
| Citations: | 182 - 10 self |
BibTeX
@ARTICLE{Pitt88computationallimitations,
author = {Leonard Pitt and Leslie G. Valiant},
title = {Computational Limitations on Learning from Examples},
journal = {Journal of the ACM},
year = {1988},
volume = {35},
pages = {965--984}
}
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Abstract
Abstract. The computational complexity of learning Boolean concepts from examples is investigated. It is shown for various classes of concept representations that these cannot be learned feasibly in a distribution-free sense unless R = NP. These classes include (a) disjunctions of two monomials, (b) Boolean threshold functions, and (c) Boolean formulas in which each variable occurs at most once. Relationships between learning of heuristics and finding approximate solutions to NP-hard optimization problems are given. Categories and Subject Descriptors: F. 1.1 [Computation by Abstract Devices]: Models of Computation-relations among models; F. 1.2 [Computation by Abstract Devices]: Modes of Computation-probabi-listic computation; F. 1.3 [Computation by Abstract Devices]: Complexity Classes-reducibility and completeness; 1.2.6 [Artificial Intelligence]: Learning-concept learning; induction
