Cited by 3,279Machine Learning and AlgorithmsComputability, Logic, AI Algorithmssemigroups and automata theory

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A theory of the learnable

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Valiant, L. G.·1984·Communications of the ACM

Abstract

Humans appear to be able to learn new concepts without needing to be programmed explicitly in any conventional sense.In this paper we regard learning as the phenomenon of knowledge acquisition in the absence of explicit programming.We give a precise methodology for studying this phenomenon from a computational viewpoint.It consists of choosing an appropriate information gathering mechanism, the learning protocol, and exploring the class of concepts that can be learned using it in a reasonable (polynomial) number of steps.Although inherent algorithmic complexity appears to set serious limits to the range of concepts that can be learned, we show that there are some important nontrivial classes of propositional concepts that can be learned in a realistic sense.

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Crossref · 10.1145/1968.1972

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Research works printed but not linked: 6

  1. Barr A., 1982, Calif.
  2. Duda R.O. and Hart P.F. Pattern Classification and Scene Analysis. Wiley New York. 1973. Duda R.O. and Hart P.F. Pattern Classification and Scene Analysis. Wiley New York. 1973.
  3. ErdSs P., 1974, Academic Press
  4. Goldreich O., 1984, Proceedings of 25th IEEE Symposium on Foundations of Computer Science (Singer Island, Fla., Oct. 24-26)
  5. Michalski R.S., 1983, Calif.
  6. Valiant L.G. Deductive learning. Philosophical Transactions of the Royal Society of London (1984). To be published. Valiant L.G. Deductive learning. Philosophical Transactions of the Royal Society of London (1984). To be published.

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Metadata

Authors
Valiant, L. G.
Affiliations
Harvard University (US)
Corresponding author
Valiant, L. G.
Venue
Communications of the ACM
Published
1984-11-05
Volume
27
Issue
11
Pages
1134-1142
ISSN
  • 0001-0782 (ISSN-L)
  • 1557-7317
OpenAlex
W4238893454
Cited by
3,279
References
3
Citations by year
2026: 58 · 2025: 93 · 2024: 117 · 2023: 106 · 2022: 123 · 2021: 116 · 2020: 96 · 2019: 91 · 2018: 89 · 2017: 94 · 2016: 89 · 2015: 84 · 2014: 62 · 2013: 91 · 2012: 71
FWCI
10.16
Type
journal article
Language
en
Open access
Locations
Communications of the ACM (publishedVersion)
Funders
National Science Foundation
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › semigroups and automata theory
Keywords
learnability, concept learning, knowledge acquisition, learning protocols, information gathering, computational complexity, algorithmic complexity, computational learning theory

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