Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Recursive neural networks and automata

  • Chapter
  • First Online:
Adaptive Processing of Sequences and Data Structures (NN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1387))

Included in the following conference series:

  • 211 Accesses

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. C. L. Giles and C. W. Omlin, “Extraction, insertion and refinement of symbolic rules in dynamically-driven recurrent neural networks,” Connection Science, vol. 5, no. 3/4, p. 307, 1993. Special Issue on Architectures for Integrating Symbolic and Neural Processes.

    Google Scholar 

  2. R. L. Watrous and G. M. Kuhn, “Induction of finite-state languages using second-order recurrent networks,” Neural Computation, vol. 4, pp. 406–414, May 1992.

    Google Scholar 

  3. H. Siegelmann and E. Sontag, “On the computational power of neural nets,” in Proceedings of the Fifth ACM Workshop on Computational Learning Theory, (New York NY), pp. 440–449, ACM, 1992.

    Google Scholar 

  4. P. Frasconi, M. Gori, M. Maggini, and G. Soda, “Representation of finite state automata in recurrent radial basis function networks,” Machine Learning, vol. 23, pp. 5–32, 1996.

    MATH  Google Scholar 

  5. J. F. Kolen, “Recurrent networks: State machines or iterated function systems?,” in Proceedings of the 1993 Connectionist Models Summer School (M. C. Mozer, P. Smolensky, D. S. Touretzky, J. L. Elman, and A. S. Weigend, eds.), (Hillsdale NJ), pp. 203–210, Erlbaum, 1994.

    Google Scholar 

  6. P. Frasconi and M. Gori, “Computational capabilities of local-feedback recurrent networks acting as finite-state machines,” IEEE Transactions on Neural Networks, vol. 7, pp. 1521–1525, November 1996.

    Article  Google Scholar 

  7. H. T. Siegelmann, B. G. Horne, and C. L. Giles, “Computational capabilities of recurrent narx neural networks,” IEEE Trans. on Systems, Man and Cybernetics-Part B: Cybernetics, vol. 27, no. 2, p. 208, 1997.

    Article  Google Scholar 

  8. H. Siegelmann and E. Sontag, “Turing computability with neural nets,” Applied Mathematics Letters, vol. 4, no. 6, pp. 77–80, 1991.

    Article  MathSciNet  MATH  Google Scholar 

  9. G. Z. Sun, H. H. Chen, Y. C. Lee, and C. L. Giles, “Turing equivalence of neural networks with second order connection weights,” in Proceedings of the International Joint Conference on Neural Networks, vol. II, pp. 357–362, 1991.

    Google Scholar 

  10. W. S. McCulloch and W. Pitts, “A logical calculus of ideas immanent in nervous activity,” Bullettin of Mathematical Biophysics, vol. 5, pp. 115–133, 1943.

    MathSciNet  MATH  Google Scholar 

  11. E. M. Gold, “Complexity of automaton identification from given data,” Information and Control, vol. 37, pp. 302–320, 1978.

    Article  MATH  MathSciNet  Google Scholar 

  12. Y. Bengio, P. Frasconi, and P. Simard, “Learning long-term dependencies with gradient descent is difficult,” IEEE Transactions on Neural Networks, vol. 5, pp. 157–166, March 1994. Special Issue on Dynamic Recurrent Neural Networks.

    Article  Google Scholar 

  13. M. F. Barnsley, Fractals Everywhere. Academic Press Professional, 1993. second edition.

    Google Scholar 

  14. C. W. Omlin and C. L. Giles, “Training second-order recurrent neural networks using hints,” in Proceedings of the Ninth International Conference on Machine Learning (D. Sleeman and P. Edwards, eds.), (San Mateo CA), pp. 363–368, Morgan Kaufmann Publishers, 1992.

    Google Scholar 

  15. S. Das and M. C. Mozer, “A unified gradient-descent/clustering architecture for finite state machine induction,” in Neural Information Processing Systems 6 (S. Cowan, G. Tesauro, and J. Alspector, eds.), pp. 19–26, 1994.

    Google Scholar 

  16. Z. Zeng, R. Goodman, and P. Smyth, “Discrete recurrent neural networks for grammatical inference,” IEEE Transactions on Neural Networks, vol. 5, pp. 320–330, March 1994. Special Issue on Dynamic Recurrent Neural Networks.

    Article  Google Scholar 

  17. M. Gori, M. Maggini, and G. Soda, “Inductive inference from noisy examples: The rule-noise dilemma and the hybrid finite state filter,” in Proceedings of ECAI96 Workshop 16 (Neural Networks and Structural Knowledge), (Budapest (Hungary)), pp. 53–58, August 1996.

    Google Scholar 

  18. E. M. Gold, “Language identification in the limit,” Information and Control, vol. 10, pp. 447–474, 1967.

    Article  MATH  Google Scholar 

  19. J. E. Hopcroft and J. D. Ullman, Introduction to Automata Theory, Languages and Computation. Reading MA: Addison-Wesley, 1979.

    MATH  Google Scholar 

  20. Z. Kohavi, Switching and Finite Automata Theory. New York, NY: McGraw-Hill, Inc., 1978. second edition.

    MATH  Google Scholar 

  21. J. Feldman, “Some decidability results on grammatical inference and complexity,” Information and Control, vol. 20, pp. 244–262, 1972.

    Article  MATH  MathSciNet  Google Scholar 

  22. D. Angluin, “On the complexity of minimum inference of regular sets,” Information and Control, vol. 39, pp. 337–350, 1978.

    Article  MATH  MathSciNet  Google Scholar 

  23. B. A. Trakhtenbrot and J. M. Barzdin, Finite Automata. Amsterdam: North-Holland, 1973.

    MATH  Google Scholar 

  24. K. S. Fu and T. L. Booth, “Grammatical inference: Introduction and survey — part I,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-5, pp. 95–111, January 1975.

    MathSciNet  Google Scholar 

  25. S. Porat and J. A. Feldman, “Learning automata from ordered examples,” Machine Learning, vol. 7, no. 2/3, pp. 5–34, 1991. Special issue on Connectionist Approaches to Language Learning.

    Google Scholar 

  26. D. Angluin, “Inferring regular sets from queries and counterexamples,” Information and Computation, vol. 75, pp. 87–106, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  27. R. L. Rivest and R. E. Schapire, “Inference of finite automata using homing sequences,” in Proceedings of the Twenty-First Annual ACM Symposium on Theory of Computing, (Seattle, WA), pp. 411–420, 1989.

    Google Scholar 

  28. M. Tomita, “Dynamic construction of finite-state automata from examples using hill-climbing,” in Proceedings of the Fourth Annual Cognitive Science Conference, (Ann Arbor MI), pp. 105–108, 1982.

    Google Scholar 

  29. J. R. Koza, Genetic Programming. MIT Press, 1992.

    Google Scholar 

  30. P. Frasconi, M. Gori, M. Maggini, and G. Soda, “Unified integration of explicit rules and learning by example in recurrent networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 7, April 1995.

    Google Scholar 

  31. J. L. Elman, “Finding structure in time,” Cognitive Sciences, vol. 14, pp. 179–211, 1990.

    Article  Google Scholar 

  32. J. L. McClelland and D. E. Rumelhart, Explorations in Parallel Distributed Processing. Cambridge: MIT Press, 1988.

    Google Scholar 

  33. D. Servan-Schreiber, A. Cleeremans, and J. L. McClelland, “Graded state machines: the representation of temporal contingencies in simple recurrent networks,” Machine Learning, vol. 7, no. 2/3, pp. 161–194, 1991. Special issue on Connectionist Approaches to Language Learning.

    Article  Google Scholar 

  34. M. W. Goudreau, C. L. Giles, S. T. Chakradhar, and D. Chen, “First-order vs. second-order single layer recurrent neural networks,” IEEE Transactions on Neural Networks, vol. 5, no. 3, p. 511, 1994.

    Article  Google Scholar 

  35. M. L. Minsky, Computation: Finite and Infinite Machines, ch. 3, pp. 32–66. Englewood Cliffs (NJ): Prentice Hall, Inc., 1967.

    MATH  Google Scholar 

  36. P. Frasconi, M. Gori, and G. Soda, “Recurrent neural networks and prior knowledge for sequence processing: a constrained nondeterministic approach,” Knowledge Based Systems, vol. 8, no. 6, pp. 313–332, 1995.

    Article  Google Scholar 

  37. C. B. Miller and C. L. Giles, “Experimental comparison of the effect of order in recurrent neural networks,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 7, no. 4, pp. 849–872, 1993. Special Issue on Applications of Neural Networks to Pattern Recognition.

    Article  Google Scholar 

  38. P. Manolios and R. Fanelli, “First-order recurrent neural networks and deterministic finite state automata,” Neural Computation, vol. 6, pp. 1155–1173, November 1994.

    Google Scholar 

  39. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, pp. 359–366, 1989.

    Article  Google Scholar 

  40. B. G. Horne and C. L. Giles, “An experimental comparison of recurrent neural networks,” in Advances in Neural Information Processing Systems (G. Tesauro, D. Touretzky, and T. Leen, eds.), vol. 7, pp. 697–704, The MIT Press, 1995.

    Google Scholar 

  41. P. Frasconi, M. Gori, and G. Soda, “Local feedback multilayered networks,” Neural Computation, vol. 4, no. 1, pp. 120–130, 1992.

    Google Scholar 

  42. A. C. Tsoi and A. D. Back, “Locally recurrent globally feedforward networks: A critical review of architectures,” IEEE Transactions on Neural Networks, vol. 5, pp. 229–239, Mar. 1994.

    Article  Google Scholar 

  43. B. Y. M. Gori, and R. De Mori, “Learning the dynamic nature of speech with back-propagation for sequences,” Pattern Recognition Letters, vol. 13, pp. 375–385, May 1992. Special issue on Artificial Neural Networks.

    Article  Google Scholar 

  44. K. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4–27, 1990.

    Article  Google Scholar 

  45. C. L. Giles, B. G. Horne, and T. Lin, “Learning a class of large finite stste machines with a recurrent neural network,” Tech. Rep. UMIACS-TR-94-94 and CS-TR-3328, Institute for Advanced Computer Studies, University of Maryland, College Park MD, August 1994.

    Google Scholar 

  46. C. W. Omlin and C. L. Giles, “Constructing deterministic finite-state automata in sparse recurrent neural networks,” in Proceedings of the IEEE International Conference on Neural Networks (ICNN'94), pp. 1732–1737, 1994.

    Google Scholar 

  47. J. B. Pollack, “The induction of dynamical recognizers,” Machine Learning, vol. 7, no. 2/3, pp. 196–227, 1991. Special issue on Connectionist Approaches to Language Learning.

    Google Scholar 

  48. R. L. Watrous and G. M. Kuhn, “Induction of finite-state automata using second-order recurrent networks,” in Advances in Neural Information Processing Systems 4, pp. 317–324, 1992.

    Google Scholar 

  49. C. L. Giles, C. B. Miller, D. Chen, G. Z. S. H. H. Chen, and Y. C. Lee, “Extracting and learning an unknown grammar with recurrent neural networks,” in Advances in Neural Information Processing Systems 4 (J. Moody, S. Hanson, and R. Lippmann, eds.), (San Mateo CA), pp. 317–324, Morgan Kauffmann Publishers, 1992.

    Google Scholar 

  50. C. L. Giles, C. B. Miller, D. Chen, G. Z. Sun, H. H. Chen, and Y. C. Lee, “Learning and extracting finite state automata with second-order recurrent neural networks,” Neural Computation, vol. 4, no. 3, pp. 393–405, 1992.

    Google Scholar 

  51. C. L. Giles and C. W. Omlin, “Rule refinement with recurrent neural networks,” in Proceedings of the IEEE International Conference on Neural Networks (ICNN'93), vol. II, pp. 801–806, 1993.

    Article  Google Scholar 

  52. M. Forcada and R. Carrasco, “Learning the initial state of a second order recurrent neural network during regular-language inference,” Neural Computation, vol. 7, no. 5, pp. 923–930, 1995.

    Google Scholar 

  53. Z. Zeng, R. Goodman, and P. Smyth, “Learning finite state machines with self-clustering recurrent networks,” Neural Computation, vol. 5, no. 6, pp. 976–990, 1993.

    Google Scholar 

  54. M. Casey, “The dynamics of discrete-time computation, with the application to recurrent neural networks and finite state machine extraction,” Neural Computation, vol. 8, no. 6, pp. 1135–1178, 1996.

    Google Scholar 

  55. D. Ron and R. Rubinfeld, “Learning fallible deterministic finite automata,” Machine Learning, vol. 18, pp. 149–185, 1995.

    MATH  Google Scholar 

  56. M. Gori, M. Maggini, and G. Soda, “Learning regular grammars from noisy examples using recurrent neural networks,” in Proceedings of the NEURAP 96, (Marseille (France)), pp. 207–214, March 20–22 1996.

    Google Scholar 

  57. R. C. Carrasco and M. L. Forcada, “Second-order recurrent neural networks can learn regular grammars from noisy strings.,” in From Natural to Artificial Neural Computatiion: Proceedings of IWANN'95 (June 7–9, 1995). (J. Mira and F. Sandoval, eds.), vol. 930 of Lecture Notes in Computer Science, pp. 605–610, Springer-Verlag, 1995.

    Google Scholar 

  58. J. Thatcher, “Tree automata: An informal survey,” in Current Trends in the Theory of Computing (A. Aho, ed.), pp. 143–172, Prentice-Hall, Inc., 1973.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

C. Lee Giles Marco Gori

Rights and permissions

Reprints and permissions

Copyright information

© 1998 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Maggini, M. (1998). Recursive neural networks and automata. In: Giles, C.L., Gori, M. (eds) Adaptive Processing of Sequences and Data Structures. NN 1997. Lecture Notes in Computer Science, vol 1387. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054002

Download citation

  • DOI: https://doi.org/10.1007/BFb0054002

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64341-8

  • Online ISBN: 978-3-540-69752-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics