Learning Classes of Probabilistic Automata

1. Paz, A.: Introduction to probabilistic automata. Academic Press, London (1971)

   MATH  Google Scholar 

2. Abe, N., Warmuth, M.: On the computational complexity of approximating distributions by probabilistic automata. Machine Learning 9, 205–260 (1992)

   MATH  Google Scholar 

3. Dempster, A., Laird, N.M., Rubin, D.B.: Maximum likelyhood from incomplete data via the em algorithm. Journal of the Royal Statistical Society 39, 1–38 (1977)

   MATH  MathSciNet  Google Scholar 

4. Baldi, P., Brunak, S.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (1998)

   Google Scholar 

5. Freitag, D., McCallum, A.: Information extraction with HMM structures learned by stochastic optimization. In: AAAI/IAAI, pp. 584–589 (2000)

   Google Scholar 

6. Gold, E.: Language identification in the limit. Inform. Control 10, 447–474 (1967)

   Article  MATH  Google Scholar 

7. Angluin, D.: Identifying languages from stochastic examples. Technical Report YALEU/DCS/RR-614, Yale University, New Haven, CT (1988)

   Google Scholar 

8. Carrasco, R., Oncina, J.: Learning stochastic regular grammars by means of a state merging method. In: ICGI, pp. 139–152. Springer, Heidelberg (1994)

   Google Scholar 

9. Carrasco, R.C., Oncina, J.: Learning deterministic regular grammars from stochastic samples in polynomial time. RAIRO 33, 1–20 (1999)

   Article  MATH  MathSciNet  Google Scholar 

10. de la Higuera, C., Thollard, F.: Identification in the limit with probability one of stochastic deterministic finite automata. In: Oliveira, A.L. (ed.) ICGI 2000. LNCS (LNAI), vol. 1891, pp. 141–156. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

11. Bergadano, F., Varricchio, S.: Learning behaviors of automata from multiplicity and equivalence queries. In: Italian Conf. on Algorithms and Complexity (1994)

    Google Scholar 

12. Beimel, A., Bergadano, F., Bshouty, N.H., Kushilevitz, E., Varricchio, S.: On the applications of multiplicity automata in learning. In: IEEE Symposium on Foundations of Computer Science, pp. 349–358 (1996)

    Google Scholar 

13. Beimel, A., Bergadano, F., Bshouty, N.H., Kushilevitz, E., Varricchio, S.: Learning functions represented as multiplicity automata. Journal of the ACM 47, 506–530 (2000)

    Article  MATH  MathSciNet  Google Scholar 

14. Thollard, F., Dupont, P., de la Higuera, C.: In: Proc. 17th ICML in title

    Google Scholar 

15. Kearns, M., Mansour, Y., Ron, D., Rubinfeld, R., Schapire, R.E., Sellie, L.: On the learnability of discrete distributions, 273–282 (1994)

    Google Scholar 

16. Esposito, Y., Lemay, A., Denis, F., Dupont, P.: Learning probabilistic residual finite state automata. In: Adriaans, P.W., Fernau, H., van Zaanen, M. (eds.) ICGI 2002. LNCS (LNAI), vol. 2484, p. 77. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

17. Denis, F., Esposito, Y.: Residual languages and probabilistic automata. In: ICALP 2003, Springer, Heidelberg (2003)

    Google Scholar 

18. Angluin, D.: Queries and concept learning. Machine Learning 2, 319–342 (1988)

    Google Scholar 

19. Vapnik, V.N.: Statistical Learning Theory. John Wiley, Chichester (1998)

    MATH  Google Scholar 

20. Lugosi, G.: Pattern classification and learning theory. In: Principles of Nonparametric Learning., pp. 1–56. Springer, Heidelberg (2002)

    Google Scholar 

21. Hardy, G.H., Wright, E.M.: An introduction to the theory of numbers. Oxford University Press, Oxford (1979)

    MATH  Google Scholar 

22. Blondel, V.D., Canterini, V.: Undecidable problems for probabilistic automata of fixed dimension. Theory of Computing Systems 36, 231–245 (2003)

    Article  MATH  MathSciNet  Google Scholar 

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