Pattern inference

  • Takeshi Shinohara
  • Setsuo Arikawa
1 Inductive Inference Theory 1.2 Inductive Inference of Formal Languages
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 961)

Abstract

A pattern is a string consisting of constant symbols and variables. The language of a pattern is the set of constant strings that are obtained by substituting nonempty constant strings for variables in the pattern. Pattern inference is a task of identifying a pattern from given examples of its language. This paper presents a survey of pattern inference from viewpoints of inductive inference from positive data and probably approximately correct (PAC) learning with typical applications.

Keywords

Polynomial Time Regular Pattern Inductive Inference Positive Data Pattern Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. [1]
    Aho, A.V. and Corasick, M.J.: Efficient string matching: An aid to bibliographic search, Communications of the ACM 18, 333–340, (1975).Google Scholar
  2. [2]
    Angluin, D.: Finding common patterns to a set of strings, In Proceedings of the 11th Annual Symposium on Theory of Computing, 130–141, (1979). (Journal of Computer and System Sciences 21, 46–62, (1980))Google Scholar
  3. [3]
    Angluin, D.: Inductive inference of formal languages from positive data, Information and Control 45, 117–135, (1980).Google Scholar
  4. [4]
    Angluin, D.: Learning regular sets from queries and counterexamples, Information and Control 75, 87–106, (1987).Google Scholar
  5. [5]
    Angluin, D.: Queries and concept learning, Machine Learning 2, 319–342, (1988).Google Scholar
  6. [6]
    Arikawa, S., Shinohara, T. and Yamamoto, A.: Learning elementary formal systems, Theoretical Computer Science 95, 97–113, (1992).Google Scholar
  7. [7]
    Arikawa, S., Kuhara, S., Miyano, S., Shinohara, A. and Shinohara, T.: A learning algorithm for elementary formal systems and its experiments on identification of transmembrane domains, In Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences, Vol. I, 675–684, (1992).Google Scholar
  8. [8]
    Arikawa, S., Miyano, S., Shinohara, A., Kuhara, S., Mukouchi, Y. and Shinohara, T.: A machine discovery from amino acid sequences by decision trees over regular patterns, New Generation Computing 11, 361–375, (1993).Google Scholar
  9. [9]
    Arimura, H., Shinohara, T. and Otsuki, S.: Finding minimal generalizations for unions of pattern languages and its application to inductive inference from positive data, In Proceedings of STACS'94, Caen, Lecture Notes in Computer Science 775, Springer-Verlag, 649–660, (1994).Google Scholar
  10. [10]
    Bairoch, A.: PROSITE: A dictionary of sites and patterns in proteins, Nucleic Acids Research 19, 2241–2245, (1991).Google Scholar
  11. [11]
    Blumer, A., Ehrenfeucht, A., Haussler, D., and Warmuth, M.: Learnability and the Vapnik-Chervonenkis dimension, Journal of the ACM 36, 929–965, (1989).Google Scholar
  12. [12]
    Chvatal, V.: A greedy heuristic for the set covering problem, Mathematics of Operations Research 4, 233–235, (1979).Google Scholar
  13. [13]
    Fujino, R.: Learning Unions of Extended Regular Pattern Languages from Positive Data and Its Application to Discovering Motifs in Proteins, Master Thesis, Department of Artificial Intelligence, Kyushu Institute of Technology, (1994).Google Scholar
  14. [14]
    Gold, E.M.: Language identification in the limit, Information and Control 10, 447–474, (1967).Google Scholar
  15. [15]
    Hartmann, E., Rapoport, T.A., and Lodish, H.F.: Predicting the orientation of eukaryotic membrane-spanning proteins, In Proceedings of the National Academy of Science of the United States of America 86, 5786–5790, (1989).Google Scholar
  16. [16]
    Haussler, D., Kearns, M., Littlestone, N., and Warmuth, M.: Equivalence of models for polynomial learnability, In Proceedings of the First Workshop on Computational Learning Theory, 34–50, (1988).Google Scholar
  17. [17]
    von Heijine, G.: A new method for predicting signal sequence cleavage sites, Nucleic Acids Research 14, 4683–4690, (1986).Google Scholar
  18. [18]
    von Heijine, G.: Transcending the impenetrable: how proteins come to terms with membranes, Biochimica et Biophysica Acta 947, 307–333, (1988).Google Scholar
  19. [19]
    Ibarra, O.H.: On two-way multihead automata, Journal of Computer and System Sciences 7, 28–36, (1973).Google Scholar
  20. [20]
    Jantke, K.P. and Beick, H.R.: Combining postulates of naturalness in inductive inference, Electron. Informationsverarb. Kybern. (EIK) 17, 465–484, (1981).Google Scholar
  21. [21]
    Jantke, K.P.: Monotonic and non-monotonic inductive inference of functions and patterns, In Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic, LNCS 543, Springer-Verlag, 161–177, (1991).Google Scholar
  22. [22]
    Jiang, T., Salomaa, A., Salomaa, K. and Yu, S.: Inclusion is undecidable for pattern languages, In Proceedings of the 20th International Colloquium, ICALP'93, Lecture Notes in Computer Science, Springer-Verlag, 301–312, (1993).Google Scholar
  23. [23]
    Kearns, M. and Pitt, L.: A Polynomial-time algorithm for learning k-variable pattern languages from examples, In Proceedings of the Second Annual Workshop on Computational Learning Theory, 57–71, (1989).Google Scholar
  24. [24]
    Ko, K. and Tzeng, W.: Three Σ2p-complete problems in computational learning theory, Computational Complexity 1, 269–310, (1991).Google Scholar
  25. [25]
    Kyte, J. and Doolittle, R.: A simple method for displaying the hydropathic character of protein, Journal of Molecular Biology 157, 105–132, (1982).Google Scholar
  26. [26]
    Lange, S. and Wiehagen, R.: Polynomial time inference of arbitrary pattern languages, New Generation Computing 8, 361–370 (1991).Google Scholar
  27. [27]
    Lange, S.: A note on polynomial-time inference of k-variable pattern languages, In Proceedings of the 1st International Workshop on Nonmonotonic and Inductive Logic, LNCS 543, Springer-Verlag, 178–183, (1991).Google Scholar
  28. [28]
    Lange, S. and Zeugmann, T.: Monotonic versus Nonmonotonic Language Learning, In Proceedings of the 2nd International Workshop on Nonmonotonic and Inductive Logic, LNCS 659, Springer-Verlag, 254–269, (1993).Google Scholar
  29. [29]
    Maier, D.: The complexity of some problems on subsequences and supersequences, Journal of the ACM 25, 322–336, (1978).Google Scholar
  30. [30]
    Miyano, S., Shinohara, A. and Shinohara, T.: Which classes of elementary formal systems are polynomial-time learnable?, In Proceedings of the 2nd Workshop on Algorithmic Learning Theory, 139–150, (1991).Google Scholar
  31. [31]
    Motoki, T., Shinohara, T. and Wright, K.: The correct definition of finite elasticity: corrigendum to identification of unions, In Proceedings of the 4th Annual Workshop on Computational Learning Theory, 375, (1991).Google Scholar
  32. [32]
    Muggleton, S., King, R., and Sternberg, M.: Using logic for protein structure prediction, In Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences, Vol.I, 685–696, (1992).Google Scholar
  33. [33]
    Mukouchi, Y.: Containment problems for pattern languages, IEICE Transactions on Information and Systems E75-D, 420–425, (1992).Google Scholar
  34. [34]
    Natarajan, B.: On learning sets and functions, Machine Learning 4, 67–97, (1991).Google Scholar
  35. [35]
    Natarajan, B.: Machine Learning — A Theoretical Approach, Morgan Kaufmann Publishers, (1991).Google Scholar
  36. [36]
    Nix, R.P.: Editing by example, ACM Trans. Program. Lang. Syst 7, 600–621, (1985).Google Scholar
  37. [37]
    PIR.: Protein identification resource, National Biomedical Research Foundation, (1991).Google Scholar
  38. [38]
    Schapire, R.: Pattern languages are not learnable, In Proceedings of the Third Annual Workshop on Computational Learning Theory, 122–129, (1990).Google Scholar
  39. [39]
    Shimozono, S., Shinohara, A., Shinohara, T., Miyano, S., Kuhara, S., and Arikawa, S.: Finding alphabet indexing for decision trees over regular patterns: an approach to bioinformatical knowledge acquisition, In Proceedings of the Twenty-Sixth Hawaii International Conference on System Sciences, Vol.I, 763–773, (1993).Google Scholar
  40. [40]
    Shinohara, T.: Polynomial time inference of pattern languages and its applications, Proceedings of the 7th IBM Symposium on Mathematical Foundations of Computer Science, 191–209, (1982).Google Scholar
  41. [41]
    Shinohara, T.: Polynomial time inference of extended regular pattern languages, RIMS Symposia on Software Science and Engineering, Kyoto, 1982, Proceedings, Lecture Notes in Computer Science 147, Springer-Verlag, 115–127, (1983).Google Scholar
  42. [42]
    Shinohara, T.: Inferring unions of two pattern languages, Bulletin of Informatics and Cybernetics 20, 83–88, (1983).Google Scholar
  43. [43]
    Shinohara, T. and Arikawa, S.: Learning data entry systems: An application of inductive inference of pattern languages, Research Report 102, Research Institute of Fundamental Information Science, Kyushu University, (1983).Google Scholar
  44. [44]
    Smullyan, R.M.: Theory of Formal Systems, Princeton University Press, Princeton, New Jersey, (1961).Google Scholar
  45. [45]
    Valiant, L.: A theory of the learnable, Communications of the ACM 27, 1134–1142, (1984).Google Scholar
  46. [46]
    Wagner, R.A. and Fischer, M.J.: The string-to-string correction problem, Journal of the ACM 21, 168–173, (1974).Google Scholar
  47. [47]
    Wright, K.: Identification of unions of languages drawn from an identifiable class, In Proceedings of the 2nd Annual Workshop on Computational Learning Theory, 328–333, (1989).Google Scholar
  48. [48]
    Zeugmann, T., Lange, S. and Kapur, S.: Characterizations of monotonic and dual-monotonic language learning, (to appear in Information and Computation).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Takeshi Shinohara
    • 1
  • Setsuo Arikawa
    • 2
  1. 1.Department of Artificial IntelligenceKyushu Institute of TechnologyIizukaJapan
  2. 2.Research Institute of Fundamental Information ScienceKyushu University 33FukuokaJapan

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