Experience-efficient learning in associative bandit problems

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 889-896

  Strehl, Alexander L ^a   Mesterharm, Chris ^a   Littman, Michael L ^a   Hirsh, Hayrti ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; FUNCTION EVALUATION; PATTERN RECOGNITION; PROBLEM SOLVING;

CLASSIFICATION ALGORITHMS; LEARNING THEORY PROBLEM; PAYOFF FUNCTIONS;

LEARNING SYSTEMS;

EID: 33749242078     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Abe, N., Biermann, A. W., & Long, P. M. (2003). Reinforcement learning with immediate rewards and linear hypotheses. Algorithmica, 37, 263-293.
2. Auer, P. (2000). An improved on-line algorithm for learning linear evaluation functions. Proceedings of the 13th Annual Conference on Computational Learning Theory (pp. 118-125).
3. Berry, D. A., & Fristedt, B. (1985). Bandit problems: Sequential allocation of experiments. London, UK: Chapman and Hall.
4. Elkan, C. (2001). The foundations of cost-sensitive learning. IJCAI (pp. 973-978).
5. Fiechter, C.-N. (1995). PAC associative reinforcement learning. Unpublished manuscript.
6. Fiechter, C.-N. (1997). Expected mistake bound model for on-line reinforcement learning. Proceedings of the Fourteenth International Conference on Machine Learning (pp. 116-124).
7. Fong, P. W. L. (1995). A quantitative study of hypothesis selection. Proceedings of the Twelfth International Conference on Machine Learning (ICML-95) (pp. 226-234).
8. Kaelbling, L. P. (1994). Associative reinforcement learning: Functions in k-DNF. Machine Learning, 15.
9. Kearns, M. J., & Schapire, R. E. (1990). Efficient distribution-free learning of probabilistic concepts. Journal of Computer and System Sciences, 48, 464-497.
10. Langford, J., & Zadrozny, B. (2005). Estimating class membership probabilities using classifier learners. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (pp. 198-205).
11. Mitchell, T. M. (1997). Machine learning. McGraw Hill.
12. Valiant, L. G. (1984). A theory of the learnable. Communications of the ACM, 27, 1134-1142.
13. Zadrozny, B., Langford, J., & Abe, N. (2003). Costsensitive learning by cost-proportionate example weighting. ICDM (p. 435).