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GRAMMATICAL COMPLEXITY AND INFERENCE

Profile image of James GipsJames Gips

Abstract

Psychology [Miller 6, Suppes 561. There is, of course, a vast literature on pattern recognition [Uhr 661, but it has been exclusively concerned with pattern descriptions which are structurally simpler than grammars.

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References (35)

  1. Aizerman, M. et. al., " Theoretical Foundations of the potential - function method in pattern recognition", Automation and Remote Control 25, 821-837 and 1175-1190 (1964).
  2. Amarel, S., " On the Automatic,Formation of a Computer Program that Represents a Theory", in Self Organizing Systems, Yovits, Jacobi, and Goldstein, edrWashington, Spartan, 1962.
  3. L- , / L. c31 Chomsky, N., and G. Miller Y "Formal Analysis of Natural Languages", PO 269-493 in Handbook of Math. Psych. II, Lute, Bush, Galantier, eds., New York, Wiley, 19631
  4. L 141 "Some Finitary Models of Language Users", in Lute et.al. (abovi), 1963b.
  5. L L r r51 161 2 "Finite-State Languages" Information and Control 1, 91-U-2 (1958). , Pattern 7, 1957. Conception, Report No. AFCRC-TN-57-57, August
  6. Church, A., Introduction to Mathematical Logic, Princeton University Press, Princeton, New Jersey,-1956.
  7. Feldman, J. A., 'First thoughts on grammatical inference", Stanford A.I. Memo No. 55, August, 1967.
  8. Ginsburg, S., The Mathematical Theory of Context-free Languages, - McGraw Hill, New York, 1966.
  9. lOI Gold, M., "Language Identification in the Limit', Information and Control, 10, 447-474 (1967).
  10. -> "Limiting Recursion" , J. Symb. Logic 30, 28-48 (1965).
  11. Gorn, S ., "Specification Languages for Mechanical Languages", Commun. ACM 4, Dec. 1961, p. 532-542.
  12. Greibach, S., "A New Normal-form Theorem for Context-free Phrase- Structure Grammars", J. ACM 12, 1 Jan. 65, p, 42-53.
  13. Harrison, M., Introduction to Switching and Automata Theory, New York, McGraw Hill, 1965.
  14. Hartmanis, J., 'Computational Complexity of one-tape Turing Machine Computations", II* ACM 15 (April 1968), pp. 325-339.
  15. Hunt, E., Marin, P. Stone, Experiments in Induction, New York, Academic Press, 1966. .
  16. Kemeny, J., J. L. Snell, and A. Knapp, Denumerable Markov Chains, D. Van Nostrand Co., Princeton, N, J., 1966. [181 Knopp, K., Theory and New York 1948. Application of Infinite Series, Hafner,
  17. Kuich, M. and K. Walk, Block Stochastic Matrices and Associated Finite-State
  18. Lederberg, J. and Inference in Languages, IBM TR 25.055 (July 1965).
  19. E. Feigenbaum, "Mechanization of Inductive Organic Chemistry", in Formal Representation of
  20. Human Judgement, Kleinmutz, ed., John Wilely, New York, 1968.
  21. London, R., "A Computer Program for Discovering and Proving Sequential Recognition Rules for BNF Grammars', Carnegie Tech., May 1964.
  22. Lute, R. D., "Selective Information Theory', in Developments in Mathematical Psychology, Lute (ed.), The Free Press, Glencoe, Illinois (1960).
  23. Miller, G., and M. Stein, "Grammarama Memos", Unpublished Internal Memos, Harvard Center for Cognitive Studies, Dec. 1963 and August 1966.
  24. Miller, W., and A. Shaw, "Linguistic Methods in Picture Processing, a Survey", Pr,oc. AFIPS FJCC, 1968, p. 279-291.
  25. Moore, E., "Gedanken Experiments on Sequential Machines", in Automata Studies, Shannon and McCarthy, Princeton, 1956.
  26. Newell, A., "Heuristic Search: Ill-Structured Problems", Progress in O.R. (Vol. 3).
  27. Q'] Perrson, S., "Some Sequence Extrapolating Programs", Stanford A.I. Memo No. 46, September 1966.
  28. Reder, S., "Introduction to Semi-rings", Unpublished research, Dept. of Mathematics, Stanford University, 1968.
  29. Reynolds, J., "Grammatical Covering", ~~-96, Argonne National Lqab., June 1968.
  30. Shamir, E., " A Remark on Discovery Algorithms for Grammars", Information and Control 5, 246-251 (1962).
  31. L [311 Shannon, G. and W. Weaver, The Mathematical Theory of Communication, University of IllinoisPress, Urbana (1949).
  32. Solomonoff, R., " Some Recent Work in Artifi'cial Intelligence", Proc. IEEE V 54, No. 12, December 1966.
  33. I 2 "A Formal Theory of Inductive Inference", Information and Control, 1964, pp. l-22, 224-254. 1343 9 "A New Method for Discovering the Grammars of Phrase L Structure Languages", Information Processing, June 1959, pp. 285-290.
  34. Suppes, P., "Concept Formation and Bayesian Decisions", in Aspects of Inductive Logic, Hintikka and Suppes, eds., 1956, Amsterdam, North Holland.
  35. %I Uhr, L., ed ., Pattern Recognition, Wiley, New York, 1966. 4

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