Mind change efficient learning

Information and Computation

Volumn 204, Issue 6, 2006, Pages 989-1011

  Luo, Wei ^a   Schulte, Oliver ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

Accumulation order; Inductive inference; Mind change complexity

Indexed keywords

LEARNING ALGORITHMS;

ACCUMULATION ORDERS; CHARACTERISTIC PROPERTIES; EFFICIENT LEARNING; INDUCTIVE INFERENCE; MIND CHANGE COMPLEXITY; MIND CHANGE EFFICIENT LEARNING; POINT-SET TOPOLOGY; STRUCTURAL COMPLEXITY;

LEARNING SYSTEMS;

EID: 84855194432     PISSN: 08905401     EISSN: 10902651     Source Type: Journal    
DOI: 10.1016/j.ic.2006.02.004     Document Type: Article

References (44)

1. A. Ambainis, S. Jain, A. Sharma, Ordinal mind change complexity of language identification, Theor. Comput. Sci. 220(2) (1999) 323-343. (Pubitemid 129609682)
2. D. Angluin, Finding patterns common to a set of strings, J. Comput. Syst. Sci. 21(1) (1980) 46-62.
3. D. Angluin, Inductive inference of formal languages from positive data, Inf. Control 45(2) (1980) 117-135.
4. K. Apsitis, Derived sets and inductive inference, in: S. Arikawa, K. P. Jantke (Eds.), Proceedings of ALT 1994, Springer, Berlin, Heidelberg, 1994, pp. 26-39.
5. P. Auer, R. Ortner, A new pac bound for intersection-closed concept classes., in: COLT, 2004, pp. 408-414. (Pubitemid 38940349)
6. G. Baliga, J. Case, S. Jain, The synthesis of language learners, Inf. Comput. 152(1999) 16-43. (Pubitemid 129608063)
7. J. D. Baum, Elements of Point Set Topology, rep. Dover, 1991, 1964.
8. G. Cantor, Grundlagen einer allgemeinen Mannigfaltigkeitslehre, in: W. Ewald (Ed.), From Kant to Hilbert, vol. 2, Oxford Science Publications, 1996, pp. 878-920.
9. R. Freivalds, C. H. Smith, On the role of procrastination in machine learning, Inf. Comput. 107(2) (1993) 237-271.
10. R. Freivalds, E. Kinber, C. H. Smith, On the intrinsic complexity of learning, Inf. Comput. 123(1) (1995) 64-71.
11. E. M. Gold, Language identification in the limit, Inf. Control 10(5) (1967) 447-474.
12. S. Jain, D. Osherson, J. S. Royer, A. Sharma, Systems That Learn, second ed., MIT Press, Cambridge, MA, 1999.
13. S. Jain, A. Sharma, The intrinsic complexity of language identification, J. Comput. Syst. Sci. 52(3) (1996) 393-402. (Pubitemid 126359767)
14. S. Jain, A. Sharma, Mind change complexity of learning logic programs, TCS 284(1) (2002) 143-160. (Pubitemid 34661532)
15. S. Jain, E. B. Kinber, R. Wiehagen, Language learning from texts: degrees of intrinsic complexity and their characterizations., J. Comput. Syst. Sci. 63(3) (2001) 305-354. (Pubitemid 34266031)
16. A. J. Jayanthan, Derived length for arbitrary topological spaces, Int. J. Math. Math. Sci. 15(2) (1992) 273-277.
17. K. Kelly, The Logic of Reliable Inquiry, Oxford University Press, 1996.
18. K. Kelly, O. Schulte, The computable testability of theories with uncomputable predictions, Erkenntnis 43(1995) 29-66.
19. K. Kelly, Efficient convergence implies Ockham's Razor, in: Proceedings of the 2002 International Workshop on Computation Models of Scientific Reasoning and Applications, 2002, pp. 24-27.
20. K. Kelly, A close shave with realism: Ockham's razor derived from efficient convergence, 2003 (completed manuscript) Available http://www.andrew.cmu.edu/user/kk3n/kelly/ockham.pdf.
21. K. Kelly, Justification as truth-finding efficiency: how ockham's razor works, Minds Mach. 14(4) (2004) 485-505. (Pubitemid 41201435)
22. S. Kocabas, Conflict resolution as discovery in particle physics, Mach. Learn. 6(1991) 277-309. (Pubitemid 21737705)
23. K. Kuratowski, Topology, vol. 1, Academic Press, 1966 (translated by J. Jaworowski).
24. S. Lange, T. Zeugmann, Language learning with a bounded number of mind changes, in: Symposium on Theoretical Aspects of Computer Science, 1993, pp. 682-691.
25. W. Luo, O. Schulte, Mind change efficient learning., in: P. Auer, R. Meir (Eds.), Learning Theory, 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings, Lecture Notes in Computer Science, vol. 3559, Springer, 2005, pp. 398-412.
26. W. Luo, Compute inclusion depth of a pattern., in: P. Auer, R. Meir (Eds.), Learning Theory, 18th Annual Conference on Learning Theory, COLT 2005, Bertinoro, Italy, June 27-30, 2005, Proceedings, Lecture Notes in Computer Science, vol. 3559, Springer, 2005, pp. 689-690.
27. E. Martin, D. N. Osherson, Elements of Scientific Inquiry, MIT Press, Cambridge, MA, 1998.
28. E. Martin, A. Sharma, F. Stephan, Learning, logic, and topology in a common framework, in: Proceedings of the 13th International Conference on Algorithmic Learning Theory, Springer-Verlag, 2002, pp. 248-262.
29. E. Martin, A. Sharma, On a syntactic characterization of classification with a mind change bound, in: COLT 2005, Lecture Notes in Computer Science, vol. 3559, Springer, 2005, pp. 413-428.
30. T. Motoki, T. Shinohara, K. Wright, The correct definition of finite elasticity: corrigendum to identification of unions, in: Proceedings of COLT 1991, Morgan Kaufmann Publishers, 1991, p. 375.
31. Y. Mukouchi, Characterization of pattern languages, in: Proc. 2nd workshop on algorithmic learning theory (ALT'91), 1991, pp. 93-104.
32. Y. Mukouchi, Inductive inference with bounded mind changes, in: S. Doshita, K. Furukawa, K. P. Jantke, T. Nishida (Eds.), Proceedings of ALT 1992, Springer, Berlin, Heidelberg, 1993, pp. 125-134.
33. P. Odifreddi, Classical Recursion Theory, North-Holland, New York, 1999.
34. D. N. Osherson, M. Stob, S. Weinstein, Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists, MIT Press, Cambridge, MA, 1986.
35. J. Pearl, Causality: Models, Reasoning, and Inference, Cambridge University Press, 2000.
36. P. Spirtes, C. Glymour, R. Scheines, Causation, Prediction, and Search, MIT Press, Cambridge, MA, 2000.
37. G. Plotkin, A note on inductive generalization, in: Machine Intelligence, vol. 5, Edinburgh University Press, 1970, pp. 153-163.
38. G. Plotkin, A further note on inductive generalization, in: Machine Intelligence, vol. 6, Edinburgh University Press, 1971, pp. 101-124.
39. O. Schulte, Automated discovery of conservation principles and new particles in particle physics, Mach. Learn. (2005) (submitted).
40. O. Schulte, An algorithmic proof that the family conservation laws are optimal for the current reaction data, arXiv preprint archive. Available from:
41. T. Shinohara, Inductive inference of monotonic formal systems from positive data, New Gen. Comput. 8(4) (1991) 371-384.
42. R. Valdés-Pérez, Algebraic reasoning about reactions: discovery of conserved properties in particle physics, Mach. Learn. 17(1994) 47-67.
43. R. Valdés-Pérez, On the justification of multiple selection rules of conservation in particle physics phenomenology, Comput. Phys. Commun. 94(1996) 25-30. (Pubitemid 126343102)
44. K. Wright, Identification of unions of languages drawn from an identifiable class, in: Proceedings of the second annual workshop on Computational learning theory, Morgan Kaufmann Publisher, 1989, pp. 328-333.