RAIRO-Theor. Inf. Appl.
Volume 33, Number 1, January Fabruary 1999
|Page(s)||1 - 19|
|Published online||15 August 2002|
- D. Angluin, Identifying languages from stochastic examples. Internal Report YALEU/DCS/RR-614 (1988).
- M. Anthony and N. Biggs, Computational learning theory. Cambridge University Press, Cambridge (1992).
- R.C. Carrasco and J. Oncina, Learning stochastic regular grammars by means of a state merging method, in Grammatical Inference and Applications, R.C. Carrasco and J. Oncina Eds., Springer-Verlag, Berlin, Lecture Notes in Artificial Intelligence 862 (1994).
- M.A. Casta no, F. Casacuberta and E. Vidal, Simulation of stochastic regular grammars through simple recurrent networks, in New Trends in Neural Computation, J. Mira, J. Cabestany and A. Prieto Eds., Springer Verlag, Lecture Notes in Computer Science 686 (1993) 210-215.
- T.M. Cover and J.A. Thomas, Elements of information theory. John Wiley and Sons, New York (1991).
- W. Feller, An introduction to probability theory and its applications, John Wiley and Sons, New York (1950).
- K.S. Fu, Syntactic pattern recognition and applications, Prentice Hall, Englewood Cliffs, N.J. (1982).
- C.L. Giles, C.B. Miller, D. Chen, H.H. Chen, G.Z. Sun and Y.C. Lee, Learning and extracting finite state automata with second order recurrent neural networks. Neural Computation 4 (1992) 393-405.
- E.M. Gold, Language identification in the limit. Inform. and Control 10 (1967) 447-474. [CrossRef]
- W. Hoeffding, Probability inequalities for sums of bounded random variables. Amer. Statist. Association J. 58 (1963) 13-30.
- J.E. Hopcroft and J.D. Ullman, Introduction to automata theory, languages and computation, Addison Wesley, Reading, Massachusetts (1979).
- K. Lang, Random DFA's can be approximately learned from sparse uniform examples, in Proc. of the 5th Annual ACM Workshop on Computational Learning Theory (1992).
- F.J. Maryanski and T.L. Booth, Inference of finite-state probabilistic grammars. IEEE Trans. Comput. C26 (1997) 521-536.
- J. Oncina and P. García, Inferring regular languages in polynomial time, in Pattern Recognition and Image Analysis, N. Pérez de la Blanca, A. Sanfeliu and E. Vidal Eds., World Scientific (1992).
- J.B. Pollack, The induction of dynamical recognizers. Machine Learning 7 (1991) 227-252.
- A.S. Reber, Implicit learning of artificial grammars. J. Verbal Learning and Verbal Behaviour 6 (1967) 855-863.
- D. Ron, Y. Singer and N. Tishby, On the learnability and usage of acyclic probabilistic finite automata, in Proc. of the 8th Annual Conference on Computational Learning Theory (COLT'95), ACM Press, New York (1995) 31-40.
- A.W. Smith and D. Zipser, Learning sequential structure with the real-time recurrent learning algorithm. Internat. J. Neural Systems 1 (1989) 125-131. [CrossRef]
- A. Stolcke and S. Omohundro, Hidden Markov model induction by Bayesian model merging, in Advances in Neural Information Processing Systems 5, C.L. Giles, S.J. Hanson and J.D. Cowan Eds., Morgan Kaufman, Menlo Park, California (1993).
- A. van der Mude and A. Walker, On the inference of stochastic regular grammars. Inform. and Control 38 (1978) 310-329. [CrossRef] [MathSciNet]
- R.L. Watrous and G.M. Kuhn, Induction of finite-state languages using second-order recurrent networks. Neural Computation 4 (1992) 406-414.
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