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Refinements of inductive inference by Popperian and reliable machines
Kybernetika, Tome 30 (1994) no. 1, pp. 23-52 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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Case, John; Jain, Sanjay; Ngo Manguelle, Suzanne. Refinements of inductive inference by Popperian and reliable machines. Kybernetika, Tome 30 (1994) no. 1, pp. 23-52. http://geodesic.mathdoc.fr/item/KYB_1994_30_1_a1/

[1] J. M. Barzdin: Prognostication of automata and functions. Inform. Process. 1 (1971), 81-84. | MR

[2] J. M. Barzdin: Two theorems on the limiting synthesis of functions. In: Theory of Algorithms and Programs, Latvian State University, Riga, 210 (1974), pp. 82-88. In Russian. | MR

[3] J. M. Barzdin, R. Freivalds: On the prediction of general recursive functions. Soviet Math. Dokl. 13 (1972), 1224-1228. | MR

[4] J. M. Barzdin, R. Freivalds: Prediction and limiting synthesis of recursively enumerable classes of functions. Latvijas Valsts Univ. Zimatm. Raksti 210 (1974), 101-111. | MR

[5] L. Blum, M. Blum: Toward a mathematical theory of inductive inference. Inform. and Control 28 (1975), 125-155. | MR | Zbl

[6] M. Blum: A machine independent theory of the complexity of recursive functions. J. Assoc. Comput. Mach. 14 (1967), 322-336. | MR | Zbl

[7] J. Case: Periodicity in generations of automata. Math. Systems Theory 8 (1974), 15-32. | MR | Zbl

[8] J. Case K. J. Chen, S. Jain: Strong separation of learning classes. J. Exper. and Theoret. Artif. Intell. 4 (1992), 281-293. | MR

[9] J. Case K. J. Chen, S. Jain: Strong separation of learning classes. In: Proc. Third International Workshop on Analogical and Inductive Inference, Dagstuhl Castle, Germany, Oct. 1992. | MR

[10] J. Case, S. Ngo Manguelle: Refinements of inductive inference by Popperian machines. Technical Report 152, SUNY/Buffalo 1979.

[11] J. Case, C. Smith: Comparison of identification criteria for machine inductive inference. Theoret. Comput. Sci. 25 (1983), 193-220. | MR | Zbl

[12] R. Daley: On the error correcting power of pluralism in BC-type inductive inference. Theoret. Comput. Sci. 24 (1983), 95-104. | MR | Zbl

[13] R. P. Daley: Inductive inference hierarchies: Probabilistic vs pluralistic. Lecture Notes in Computer Science 215 (1986), 73-82. | Zbl

[14] M. Fulk: A Study of Inductive Inference machines. PhD thesis, SUNY at Buffalo 1985.

[15] M. Fulk: Saving the phenomenon: Requirements that inductive machines not contradict known data. Inform. and Comput. 79 (1988), 193-209. | MR

[16] E. M. Gold: Language identification in the limit. Inform. and Control 10 (1067), 447-474. | Zbl

[17] M. Machtey, P. Young: An Introduction to the General Theory of Algorithms. North Holland, New York 1978. | MR | Zbl

[18] Y. Marcoux: Composition is almost as good as s-1-1. In: Proc. Structure in Complexity Theory--Fourth Annual Conference. IEEE Computer Society Press, 1989.

[19] E. Minicozzi: Some natural properties of strong identification in inductive inference. Theoret. Comput. Sci. (1976), 345-360. | MR | Zbl

[20] D. Osherson M. Stob, S. Weinstein: Systems that Learn, An Introduction to Learning Theory for Cognitive and Computer Scientists. MIT Press, Cambridge, Mass. 1986.

[21] L. Pitt, C. Smith: Probability and plurality for aggregations of learning machines. Inform. and Comput. 77 (1988), 77-92. | MR | Zbl

[22] K. Podnieks: Comparing various concepts of function prediction. Part I. Theory of Algorithms and Programs 210 (1974), 68-81.

[23] K. Popper: The Logic of Scientific Discovery. Harper Torch Books, New York 1968.

[24] G. Riccardi: The Independence of Control Structures in Abstract Programming Systems. PhD thesis, SUNY at Buffalo 1980. | MR

[25] G. Riccardi: The independence of control structures in abstract programming systems. J. Comput. System Sci. 22 (1981), 107-143. | MR | Zbl

[26] H. Rogers: Gödel numberings of partial recursive functions. J. Symbolic Logic 23 (1958), 331-341. | MR

[27] H. Rogers: Theory of Recursive Functions and Effective Computability. McGraw Hill, New York 1967. Reprinted, MIT Press 1987. | MR | Zbl

[28] J. Royer: A Connotational Theory of Program Structure. (Lecture Notes in Computer Science 273.) Springer-Verlag, Berlin 1987. | MR | Zbl

[29] C. Smith: The power of pluralism for automatic program synthesis. J. Assoc. Math. Comput. Mach. 29 (1982), 1144-1165. | MR | Zbl

[30] T. Zeugmann: A-posteriori characterizations in inductive inference of recursive functions. Electron. Informationsverarb. u. Kybernetik 19 (1983), 559-594. | MR | Zbl