A new family of weak estimators for training in non-stationary distributions

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3138, Issue , 2004, Pages 644-652

  John Oommen, B ^a   Rueda, Luis ^b  

^a CARLETON UNIVERSITY   (Canada)

^b UNIVERSITY OF WINDSOR   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ESTIMATION; PATTERN RECOGNITION; STOCHASTIC SYSTEMS;

BINOMIAL DISTRIBUTION; EMPIRICAL ANALYSIS; ESTIMATION METHODS; LEARNING COEFFICIENTS; PATTERN RECOGNITION PROBLEMS; STOCHASTIC LEARNING; UNDERLYING DISTRIBUTION; WEAK ESTIMATORS;

DATA COMPRESSION;

EID: 35048859793     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Article

References (17)

1. G. Casella and R. Berger. Statistical Inference. Brooks/Cole Pub. Co., second edition, 2001.
2. R. Duda, P. Hart, and D. Stork. Pattern Classification. John Wiley and Sons, Inc., New York, NY, 2nd edition, 2000.
3. S. Lakshmivarahan. Learning Algorithms Theory and Applications. Springer-Verlag, New York, 1981.
4. S. Lakshmivarahan and M. A. L. Thathachar. Absolutely Expedient Algorithms for Stochastic Automata. IEEE Trans. on System, Man and Cybernetics, SMC-3:281-286, 1973.
5. J. K. Lanctôt and B. J. Oommen. Discretized Estimator Learning Automata. IEEE Trans. on Systems, Man and Cybernetics, 22(6): 1473-1483, 1992.
6. K. Narendra and M. Thathachar. Learning Automata. An Introduction. Prentice Hall, 1989.
7. J. Norris. Markov Chains. Springer Verlag, 1999.
8. B.J. Oommen. Absorbing and Ergodic Discretized Two-Action Learning Automata. IEEE Trans. on System, Man and Cybernetics, SMC-16:282-296, 1986.
9. B.J. Oommen and M. Agache. Continuous and Discretized Pursuit Learning Schemes: Various Algorithms and Their Comparison. IEEE Trans. on Systems, Man and Cybernetics, SMC-31(B):277-287, June 2001.
10. B.J. Oommen and J. R. P. Christensen. Epsilon-Optimal Discretized Reward-Penalty Learning Automata. IEEE Trans. on System, Man and Cybernetics, SMC-18:451-458, 1988.
11. B.J. Oommen and J. K. Lanctôt. Discretized Pursuit Learning Automata. IEEE Trans. on System, Man and Cybernetics, 20(4):931-938, 1990.
12. B.J. Oommen and L. Rueda. Stochastic Learning-based Weak Estimation of Multinomial Random Variables and Its Applications to Non-stationary Environments. (Submitted for Publication).
13. G. I. Papadimitriou. A New Approach to the Design of Reinforcement Schemes for Learning Automata: Stochastic Estimator Learning Algorithms. IEEE Trans. on Knowledge and Data Engineering, 6:649-654, 1994.
14. K. Rajaraman and P. S. Sastry. Finite Time Analysis of the Pursuit Algorithm for Learning Automata. IEEE Trans. on Systems, Man and Cybernetics, 26(4):590-598, 1996.
15. S. Ross. Introduction to Probability Models. Academic Press, second edition, 2002.
16. M. A. L. Thathachar and P.S. Sastry. A Class of Rapidly Converging Algorithms for Learning Automata. IEEE Trans. on Systems, Man and Cybernetics, SMC-15:168-175, 1985.
17. A. Webb. Statistical Pattern Recognition. John Wiley & Sons, N.York, second edition, 2002.