Note on the utility of incremental learning

AI Communications

Volumn 13, Issue 4, 2000, Pages 215-223

  Giraud Carrier, Christophe ^a  

^a UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INTELLIGENT AGENTS; LEARNING ALGORITHMS; PROBLEM SOLVING;

INCREMENTAL LEARNING ALGORITHMS; INDUCTIVE LEARNING;

LEARNING SYSTEMS;

EID: 0034499376     PISSN: 09217126     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (32)

1. M. Anthony and N. Biggs, Computational Learning Theory, Cambridge University Press, 1997.
2. P. Brazdil, Learning in multi-agent environments, in: Proceedings of the European Working Session on Learning, 1991.
3. P. Brazdil, M. Gams, S. Sian, L. Torgo and W. van de Velde, Learning in distributed systems and multi-agent environments, in: Proceedings of the Second Workshop on Algorithmic Learning Theory, 1991, pp. 15-29.
4. G. Brewka, Nonmonotonic Reasoning: Logical Foundations of Commonsense, Cambridge University Press, 1991.
5. P. Clark and T. Niblett, The CN2 induction algorithm, Machine Learning 3 (1989), 261-283.
6. A. Cornuéjols, An exploration into incremental learning: the INFLUENCE system, in: Proceedings of the Sixth International Conference on Machine Learning, 1989, pp. 383-386.
7. A. Cornuéjols, Getting order independence in incremental learning, in: Proceedings of the Sixth European Conference on Machine Learning, 1993, pp. 196-212.
8. A. Cornuéjols, Training issues in incremental learning, AAAI-93 Spring Symposium, Report SS-93-06, AAAI Press, 1993.
9. T.L. Dean and M. Boddy, An analysis of time-dependent planning, in: Proceedings of the National Conference on Artificial Intelligence, 1988, pp. 49-54.
10. J.L. Elman, Incremental learning, or the importance of starting small, CRL Technical report 9101, University of California, San Diego, Center for Research in Language, 1991.
11. D. Fisher, Knowledge acquisition via incremental conceptual clustering, Machine Learning 2 (1987), 139-172.
12. D. Fisher, L. Xu and N. Zard, Ordering effects in clustering, in: Proceedings of the Eighth International Conference on Machine Learning, 1992.
13. A. Ginsberg, Theory reduction, theory revision and retranslation, in: Proceedings of the Eighth National Conference on Artificial Intelligence, 1990, pp. 777-782.
14. C. Giraud-Carrier and T.R. Martinez, Using precepts to augment training set learning, in: Proceedings of the First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, 1993, pp. 46-51.
15. C. Giraud-Carrier and T. Martinez, An efficient metric for heterogeneous inductive learning applications in the attribute-value language, in: Proceedings of the Fourth Golden West International Conference on Intelligent Systems, E.A. Yfantis, ed., Vol. 1, 1994, pp. 341-350.
16. C. Giraud-Carrier and T. Martinez, ILA: Combining inductive learning with prior knowledge and reasoning, Technical report CSTR-95-03, University of Bristol, Department of Computer Science, 1995.
17. L.P. Kaelbling, Reinforcement learning, in: MLnet Workshop: Learning in Autonomous Agents, (invited talk), 1987.
18. P. Langley, Personal communication, 1999.
19. W. Lukaszewicz, Non-Monotonic Reasoning: Formalization of Commonsense Reasoning, Ellis Horwood Limited, 1990.
20. J. MacGregor, The effects of order in learning classifications by example: Heuristics for finding the optimal order, Artificial Intelligence 34 (1988), 361-370.
21. T.R. Martinez, B. Hughes and D.M. Campbell, Priority ASOCS, Journal of Artificial Neural Networks 1(3) (1994), 403-429.
22. R.S. Michalski, A theory and methodology of inductive learning, Artificial Intelligence 20 (1983), 111-161.
23. T.M. Mitchell, Generalization as search, Artificial Intelligence 18 (1982), 203-226.
24. T.M. Mitchell, Machine Learning, McGraw-Hill, 1997.
25. A. Newell and H. Simon, Computer science as empirical enquiry: Symbols and search, Communications of the ACM 19(3) (1976), 113-126.
26. D. Ourston and R.J. Mooney, Theory refinement combining analytical and empirical methods, Artificial Intelligence 66(2) (1994), 273-309.
27. J.R. Quinlan, Inductive learning of decision trees, Machine Learning 1 (1986), 81-106.
28. D. Rumelhart and J. McClelland, Parallel Distributed Processing, Vol. 1, MIT Press, 1986.
29. H. Simon, Why should machines learn?, in: Machine Learning: An Artificial Intelligence Approach, R.S. Michalski, J.G. Carbonell and T.M. Mitchell, eds, Tioga Publishing Company, Palo Alto, CA, 1983.
30. M. Tan, Adaptation-based cooperative learning multiagent systems, in: Proceedings of the Tenth International Conference on Machine Learning, 1993, pp. 330-337.
31. G.G. Towell, J.W. Shavlik and M.O. Noordewier, Refinement of approximate domain theories by knowledge-based neural networks, in: Proceedings of the Eighth National Conference on Artificial Intelligence, 1990, pp. 861-866.
32. P. Utgoff, ID5: An incremental ID3, in: Proceedings of the Fifth International Workshop on Machine Learning, 1988, pp. 107-120.