Journal of the ACM Bibliography

Categories and Subject Descriptors: F.1.m [Computation by Abstract Devices]: Miscellaneous; I.2.2 [Artificial Intelligence]: Automatic Programming -- program synthesis; I.2.6 [Artificial Intelligence]: Learning

General Terms: Theory

Additional Key Words and Phrases: Recursion theory, inductive inference machines, algorithmic learning by example

Selected papers that cite this one

• Kalvis Aps{\=\i}tis, R\=usi\c{n}\v{s} Freivalds, and Carl H. Smith. Choosing a learning team: a topological approach. In Proceedings of the Twenty-Sixth Annual ACM Symposium on the Theory of Computing, pages 283-289, Montréal, Québec, Canada, 23-25 May 1994.

• Kalvis Aps{\=\i}tis, R\=usi\c{n}\v{s} Freivalds, and Carl H. Smith. On duality in learning and the selection of learning terms. Information and Computation, 129(1):53-62, 25 August 1996.

• Ganesh Baliga, Sanjay Jain, and Arun Sharma. Learning from multiple sources of inaccurate data. SIAM Journal on Computing, 26(4):961-990, August 1997.

• John Case, Susanne Kaufmann, Efim Kinber, and Martin Kummer. Learning recursive functions from approximations. Journal of Computer and System Sciences, 55(1):183-196, August 1997.

• Robert Daley, Bala Kalyanasundaram, and Mahendran Velauthapillai. Breaking the probability 1/2 barrier in FIN-type learning. Journal of Computer and System Sciences, 50(3):574-599, June 1995.

• Anna-Maria Emde and Britta Schinzel. Aggregating inductive expertise on partial recursive functions. Information and Computation, 96(2):139-167, February 1992.

• Lance Fortnow, R\=usi\c{n}\v{s} Freivalds, William I. Gasarch, Martin Kummer, Stuart A. Kurtz, Carl H. Smith, and Frank Stephan. On the relative sizes of learnable sets. Theoretical Computer Science, 197(1-2):139-156, 15 May 1998.

• R\=usi\c{n}\u{s} Freivalds and Carl H. Smith. On the role of procrastination in machine learning. Information and Computation, 107(2):237-271, December 1993.

• William I. Gasarch and Carl H. Smith. Learning via queries. Journal of the ACM, 39(3):649-674, July 1992.

• Sanjay Jain and Arun Sharma. On aggregating teams of learning machines. Theoretical Computer Science, 137(1):85-108, 9 January 1995.

• Sanjay Jain and Arun Sharma. Learning in the presence of partial explanations. Information and Computation, 95(2):162-191, December 1991.

• Sanjay Jain and Arun Sharma. Learning with the knowledge of an upper bound on program size. Information and Computation, 102(1):118-166, January 1993.

• Sanjay Jain and Arun Sharma. Computational limits on team identification of languages. Information and Computation, 130(1):19-60, 10 October 1996.

• Sanjay Jain, Arun Sharma, and Mahendran Velauthapillai. Finite identification of functions by teams with success ratio 1/2 and above. Information and Computation, 121(2):201-213, September 1995.

• Efim Kinber, Carl H. Smith, Mahendran Velauthapillai, and Rolf Wiehagen. On learning multiple concepts in parallel. Journal of Computer and System Sciences, 50(1):41-52, February 1995.

• Martin Kummer and Frank Stephan. On the structure of degrees of inferability. Journal of Computer and System Sciences, 52(2):214-238, April 1996.

• Léa Meyer. Probabilistic language learning under monotonicity constraints. Theoretical Computer Science, 185(1):81-128, 10 October 1997.

Selected references

• Lenore Blum and Manuel Blum. Toward a mathematical theory of inductive inference. Information and Control, 28(2):125-155, June 1975.

• John Case and Carl Smith. Anomaly hierarchies of mechanized inductive inference. In Conference Record of the Tenth Annual ACM Symposium on Theory of Computing, pages 314-319, San Diego, California, 1-3 May 1978.

• Jerome Feldman. Some decidability results on grammatical inference and complexity. Information and Control, 20(3):244-262, April 1972.

• E. Mark Gold. Language identification in the limit. Information and Control, 10(5):447-474, May 1967.

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