Teaching randomized learners

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

Volumn 4005 LNAI, Issue , 2006, Pages 229-243

  Balbach, Frank J ^a   Zeugmann, Thomas ^b  

^a UNIVERSITY OF LÜBECK   (Germany)

^b HOKKAIDO UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDBACK CONTROL; KNOWLEDGE ACQUISITION; PARAMETER ESTIMATION; PERSONNEL; THEOREM PROVING;

LEARNER'S MEMORY SIZE; TEACHING RANDOMIZED LEARNERS;

LEARNING SYSTEMS;

EID: 33746075565     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11776420_19     Document Type: Conference Paper

References (19)

1. D.Angluin. Queries and concept learning. Machine Learning, 2(4):319-342, 1988.
2. D. Angluin. Queries revisited. Theoret. Comput. Sci., 313(2):175-194, 2004.
3. D. Angluin and M. Kriķis. Teachers, learners and black boxes, In Proc. of the 10th Annual Conference on Computational Learning Theory, pages 285-297. ACM Press, New York, NY, 1997.
4. D. Angluin and M. Kriķis. Learning from different teachers. Machine Learning, 51(2):137-163, 2003.
5. M. Anthony, G. Brightwell, D. Cohen, and J. Shawe-Taylor. On exact specification by examples. In Proc. of the 5th Annual ACM Workshop on Computational Learning Theory, pages 311-318. ACM Press, New York, NY, 1992.
6. D. P. Bertsekas. Dynamic Programming and Optimal Control. Athena Scientific.
7. U. Feige. A threshold of In n for approximating set cover. J. of the ACM, 45(4):634-652, 1998.
8. R. Freivalds, E. B. Kinber, and R. Wiehagen. On the power of inductive inference from good examples. Theoret. Comput. Sci., 110(1):131-144, 1993.
9. S. A. Goldman and M. J. Kearns. On the complexity of teaching. J. Comput. Syst. Sci., 50(1):20-31, 1995.
10. S. A. Goldman and H. D. Mathias. Teaching a smarter learner. J. Comput. Syst. Sci., 52(2):255-267, 1996.
11. S. A. Goldman, R. L. Rivest, and R. E. Schapire. Learning binary relations and total orders. SIAM J. on Computing, 22(5):1006-1034, Oct. 1993.
12. J. Jackson and A. Tomkins. A computational model of teaching. In Proc. of the 5th Annual ACM Workshop on Computational Learning Theory pages 319-326. ACM Press, New York, NY, 1992.
13. S. Jain, S. Lange, and J. Nessel. Learning of r.e. languages from good examples. In Algorithmic Learning Theory, 8th International Workshop, ALT '97, Sendai, Japan, October 1997, Proceedings, Lecture Notes in Artificial Intelligence Vol. 1316, pages 32-47, Springer, 1997.
14. O. Madani, S. Hanks, and A. Condon. On the undecidability of probabilistic planning and infinite-horizon partially observable Markov decision problems. In Proc. of the 16th National Conference on Artificial Intelligence and 11th Conference on Innovative Applications of Artificial Intelligence, pages 541-548, AAAI Press/The MIT Press, 1999.
15. H. D. Mathias. A model of interactive teaching. J. Comput. Syst. Sci., 54(3):487-501, 1997.
16. M. Mundhenk, J. Goldsmith, C. Lusena, and E. Allender. Complexity of finitehorizon Markov decision process problems. J. of the ACM, 47(4):681-720, 2000.
17. M. L. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, 1994.
18. R. Raz and S. Safra. A sub-constant error-probability low-degree test, and a subconstant error-probability PCP characterization of NP. In Proc. of the 29th ACM Symposium on Theory of Computing, pages 475-484, 1997.
19. A. Shinohara and S. Miyano. Teachability in computational learning. New Generation Computing, 8(4):337-348, 1991.