On the computational complexity of approximating distributions by probabilistic automata-Reference-Cited by-同舟云学术

On the computational complexity of approximating distributions by probabilistic automata

Published:1992-07 Issue:2-3 Volume:9 Page:205-260
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Abe Naoki,Warmuth Manfred K.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/content/pdf/10.1007/BF00992677.pdf

Reference20 articles.

1. Abe, N., Takeuchi, J., & Warmuth, M. K., (1991). Polynomial learnability of probabilistic concepts with respect to the Kullback-Leibler divergence. InProceedings of the 1991 Workshop on Computational Learning Theory. San Mateo, CA: Morgan Kaufmann.

2. Angluin, D., (1978). On the complexity of minimal inference of regular sets.Information and Control, 39 337?350.

3. Angluin, D., (1988).Identifying languages from stochastic examples (Technical Report YALEU/DCS/RR-614). Yale University.

4. Angluin, D., (1989).Minimum consistent 2-state DFA problem is NP-complete. Unpublished manuscript.

5. Barron, A.R. & Cover, T.M., (1989). Minimum complexity density estimation.IEEE Transactions on Information Theory.

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