The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Computational limitations on learning from examples
Open accessAbstract
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.
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Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
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Cited by
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The Strength of Weak Learnability
Schapire, R. E. (1990) · Machine Learning · doi:10.1007/bf00116037 · Cited by 4,600
Learnability and the Vapnik-Chervonenkis dimension
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1989) · Journal of the ACM · doi:10.1145/76359.76371 · Cited by 1,839
Blumer, A., Ehrenfeucht, A., Haussler, D., & Warmuth, M. K. (1987) · Information Processing Letters · doi:10.1016/0020-0190(87)90114-1 · Cited by 1,054
Property testing and its connection to learning and approximation
Goldreich, O., Goldwasser, S., & Ron, D. (1998) · Journal of the ACM · doi:10.1145/285055.285060 · Cited by 1,020
Metadata
- Authors
- Pitt, L., & Valiant, L. G.
- Venue
- Journal of the ACM
- Published
- 1988-10
- Volume
- 35
- Issue
- 4
- Pages
- 965-984
- ISSN
- 0004-5411 (ISSN-L)
- 1557-735X
- OpenAlex
- W2117049614
- Cited by
- 515
- References
- 1
- FWCI
- 36.94
- Type
- journal article
- Language
- en
- Open access
- bronze
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