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A theory of the learnable

Author:

L. G. ValiantAuthors Info & Claims

Communications of the ACM, Volume 27, Issue 11

Pages 1134 - 1142

https://doi.org/10.1145/1968.1972

Published: 05 November 1984 Publication History

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References

[1]

Angluin, D., Smith, C.H. Inductive inference: Theory and methods. Comput. Surv. 15, 3 (Sept. 1983), 237-269.

Digital Library

Google Scholar

[2]

Barr, A., and Feigenbaum, E.A. The Handbook of Artificial Intelligence. Vol. 2. William Kaufmann, Los Altos, Calif., 1982.

Google Scholar

[3]

Cook, S.A. The complexity of theorem proving procedures. In Proceedings of 3rd Annual ACM Symposium on Theory of Computing (Shaker Heights, Ohio, May 3-5). ACM, New York, 1971, 151-158.

Digital Library

Google Scholar

[4]

Duda, R.O., and Hart, P.F. Pattern Classification and Scene Analysis. Wiley, New York. 1973.

Google Scholar

[5]

ErdSs, P., and Spencer, J. Probabilistic Methods in Combinatorics. Academic Press, New York, 1974.

Google Scholar

[6]

Goldreich, O., Goldwasser, S., and Micali, S. How to construct random functions. In Proceedings of 25th IEEE Symposium on Foundations of Computer Science (Singer Island, Fla., Oct. 24-26). IEEE, New York, 1984.

Google Scholar

[7]

Michalski, R.S. Carbonell, J.G., and Mitchell, T.M. Machine Learning: An Artificial Intelligence Approach. Tioga Publishing Co., Palo Alto, Calif., 1983.

Digital Library

Google Scholar

[8]

Skyum, S., and Valiant, L.G. A complexity theory based on Boolean algebra. In Proceedings of 22rid IEEE Symposium on Foundations of Computer Science (Nashville, Tenn., Oct. 28-30). IEEE, New York, 1981, 244-253.

Digital Library

Google Scholar

[9]

Valiant, L.G. Deductive learning. Philosophical Transactions of the Royal Society of London (1984). To be published.

Google Scholar

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Singh AEaswaran ADastani MSichman JAlechina NDignum V(2024)PAS: Probably Approximate Safety Verification of Reinforcement Learning Policy Using Scenario OptimizationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663036(1745-1753)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663036
Figuera PGarcía Bringas P(2024)Revisiting Probabilistic Latent Semantic Analysis: Extensions, Challenges and InsightsTechnologies10.3390/technologies1201000512:1(5)Online publication date: 3-Jan-2024
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Index Terms

A theory of the learnable
1. Computing methodologies
  1. Machine learning
    1. Learning settings
2. Human-centered computing
  1. Human computer interaction (HCI)

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Freivalds and Smith [R. Freivalds, C.H. Smith Memory limited inductive inference machines, Springer Lecture Notes in Computer Science 621 (1992) 19-29] proved that probabilistic limited memory inductive inference machines can learn with probability 1 ...

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Published In

Communications of the ACM Volume 27, Issue 11

Nov. 1984

66 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/1968

Editor:
Peter J. Denning
NASA Ames Research Center, Moffett Field, CA

Issue’s Table of Contents

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Association for Computing Machinery

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Publication History

Published: 05 November 1984

Published in CACM Volume 27, Issue 11

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Singh AEaswaran ADastani MSichman JAlechina NDignum V(2024)PAS: Probably Approximate Safety Verification of Reinforcement Learning Policy Using Scenario OptimizationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems10.5555/3635637.3663036(1745-1753)Online publication date: 6-May-2024
https://dl.acm.org/doi/10.5555/3635637.3663036
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From inductive inference to algorithmic learning theory

Non-U-shaped vacillatory and team learning

Quantum inductive inference by finite automata

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