Mind change efficient learning

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

Volumn 3559 LNAI, Issue , 2005, Pages 398-412

  Luo, Wei ^a   Schulte, Oliver ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER PROGRAMMING LANGUAGES; LEARNING ALGORITHMS; PROBLEM SOLVING;

CONJECTURES; LANGUAGE CLASSES; LEARNING PROBLEMS; OPTIMAL LEARNERS;

LEARNING SYSTEMS;

EID: 26944475158     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11503415_27     Document Type: Conference Paper

References (29)

1. A. Ambainis, S. Jain, and A. Sharma. Ordinal mind change complexity of language identification. Theor. Comput. Sci., 220(2):323-343, 1999.
2. D Angluin. Finding patterns common to a set of strings. J. Comput. Syst. Sci., 21(1):46-62, 1980.
3. D Angluin. Inductive inference of formal languages from positive data. Information and Control, 45(2):117-135, 1980.
4. K. Apsitis. Derived sets and inductive inference. In S. Arikawa and K. P. Jantke, editors, Proceedings of ALT 1994, pages 26-39. Springer, Berlin, Heidelberg, 1994.
5. G. Baliga, J. Case, and S. Jain. The synthesis of language learners. Information and Computation, 152:16-43, 1999.
6. G. Cantor. Grundlagen einer allgemeinen Mannigfaltigkeitslehre. In William Ewald, editor, From Kant to Hilbert, volume 2, pages 878-920. Oxford Science Publications, 1996.
7. R. Freivalds, E. Kinber, and C. H. Smith. On the intrinsic complexity of learning. Inf. Comput., 123(1):64-71, 1995.
8. R. Freivalds and C. H. Smith. On the role of procrastination in machine learning. Inf. Comput., 107(2):237-271, 1993.
9. E. Mark Gold. Language identification in the limit. Information and Control, 10(5):447-474, 1967.
10. S. Jain, D. Osherson, J. S. Royer, and A. Sharma. Systems That Learn. M.I.T. Press, 2 edition, 1999.
11. S. Jain and A. Sharma. The intrinsic complexity of language identification. J. Comput. Syst. Sci., 52(3):393-402, 1996.
12. S. Jain and A. Sharma. Mind change complexity of learning logic programs. TCS, 284(1):143-160, 2002.
13. A. J. Jayanthan. Derived length for arbitrary topological spaces. International Journal of Mathematics and Mathematical Sciences, 15(2):273-277, 1992.
14. K. Kelly. The Logic of Reliable Inquiry. Oxford University Press, 1996.
15. K. Kelly. Efficient convergence implies Ockham's Razor. In Proceedings of the 2002 International Workshop on Computation Models of Scientific Reasoning and Applications, pages 24-27, 2002.
16. K. Kelly. Justification as truth-finding efficiency: How ockham's razor works. Minds and Machines, 14(4):485-505, 2004.
17. S. Kocabas. Conflict resolution as discovery in particle physics. Machine Learning, 6:277-309, 1991.
18. K. Kuratowski. Topology, volume 1. Academic Press, 1966. Translated by J. Jaworowski.
19. S. Lange and T. Zeugmann. Language learning with a bounded number of mind changes. In Symposium on Theoretical Aspects of Computer Science, pages 682-691, 1993.
20. E. Martin and D. N. Osherson. Elements of Scientific Inquiry. The MIT Press, Cambridge, Massachusetts, 1998.
21. E. Martin, A. Sharma, and F. Stephan, Learning, logic, and topology in a common frame-work. In Proceedings of the 13th International Conference on Algorithmic Learning Theory, pages 248-262. Springer-Verlag, 2002.
22. T. Motoki, T. Shinohara, and K. Wright. The correct definition of finite elasticity: corrigendum to identification of unions. In Proceedings of COLT 1991, page 375. Morgan Kaufmann Publishers Inc., 1991.
23. Y. Mukouchi. Inductive inference with bounded mind changes. In S. Doshita, K. Furukawa, K. P. Jantke, and T. Nishida, editors, Proceedings of ALT 1992, pages 125-134. Springer, Berlin, Heidelberg, 1993.
24. P. Odifreddi. Classical Recursion Theory. North-Holland, October 1999.
25. D. N. Osherson, M. Stob, and S. Weinstein. Systems that learn: an introduction to learning theory for cognitive and computer scientists. MIT Press, 1986.
26. O. Schulte. Automated discovery of conservation principles and new particles in particle physics. Manuscript submitted to Machine Learning, 2005.
27. R. Valdés-Pérez. Algebraic reasoning about reactions: Discovery of conserved properties in particle physics. Machine Learning, 17:47-67, 1994.
28. R. Valdés-Pérez. On the justification of multiple selection rules of conservation in particle physics phenomenology. Computer Physics Communications, 94:25-30, 1996.
29. K. Wright. Identification of unions of languages drawn from an identifiable class. In Proceedings of the second annual workshop on Computational learning theory, pages 328-333. Morgan Kaufmann Publishers Inc., 1989.