Constructive induction: A version space-based approach

IJCAI International Joint Conference on Artificial Intelligence

Volumn 2, Issue , 1999, Pages 708-713

  Sebag, Michele ^a,b  

^a LABORATOIRE DE MÉCANIQUE DES SOLIDES   (France)

^b UFR 919 Laboratoire d'Informatique Pour la Mécanique et les Sciences de l'Ingénieur   (France)

Author keywords

[No Author keywords available]

Indexed keywords

BENCH-MARK PROBLEMS; CONSTRUCTIVE INDUCTION; LEARNING STRATEGY; POLYNOMIAL NUMBER; PROBLEM DOMAIN; SPACE-BASED; TRAINING SETS; VERSION SPACE;

ARTIFICIAL INTELLIGENCE;

EID: 84880677072     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. th ICML., pages 19-27. Morgan Kaufmann, 1998.
2. L. Breiman, J.H. Friedman, R.A. 01- shen, and C.J. Stone. Classification and Regression by tree. Wadsworth, Belmont California, 1984.
3. E. Keogh C. Blake and C.J. Merz. UCI Repository of machine learning databases. http://www.ics.uci.edu/-mlearn/MLRepository.html, 1998.
4. M.W. Craven and J.W. Shavlik. Learning to represent codons: A challenge problem for constructive induction. In Proc. of IJCAI-93, pages 1319-1324. Morgan Kaufmann, 1993.
5. T.G. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, to appear, 1998.
6. th ICML., pages 148-156. Morgan Kaufmann, 1996.
7. th ICML., pages 206-214. Morgan Kaufmann, 1998.
8. D. Haussler. Quantifying inductive bias : AI learning algorithms and Valiant's learning framework. Artificial Intelligence, 36:177-221, 1988. (Pubitemid 18655803)
9. R.C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11:63-90, 1993.
10. R. Khardon and D. Roth. Learning to reason. Jal of the ACM, 44(5):697-725, 1997. (Pubitemid 127614257)
11. S. Kramer, B. Pfahringer, and C.- Helma. Stochastic propositionalization of non-determinate background knowledge. In D. Page, ed., Proc. of Inductive Logic Programming'98, pages 29-61, 1998.
12. R.S. Michalski. A theory and methodology of inductive learning. In R.S Michalski, J.G. Carbonell, and T.M. Mitchell, eds, Machine Learning: an artificial intelligence approach, volume 1, pages 83-134. Morgan Kaufmann, 1983.
13. T.M. Mitchell. Generalization as search. Artificial Intelligence, 18:203-226, 1982.
14. S. Muggleton. Inverse entailment and PROGOL. New Gen. Comput., 13:245-286, 1995.
15. th IWML, pages 183-187. Morgan Kaufmann, 1991.
16. th ICML., pages 447-455. Morgan Kaufmann, 1995.
17. J. Pitman. Probability. Springer Verlag, 1993.
18. J.R. Quinlan. Learning logical definition from relations. Machine Learning, 5:239-266, 1990.
19. J.R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.
20. B. Scholkopf, C. Burgess, and A. Smola. Advances in Kernel Methods. MIT Press, 1998.
21. M. Sebag and C. Rouveirol. Tractable induction and classification in FOL via Stochastic Matching. In Proc. of IJCAI-97, pages 888-892. Morgan Kaufmann, 1997.
22. th ICML., pages 444-452. Morgan Kaufmann, 1996.
23. A. Srinivasan. The predictive toxicology evaluation challenge. In Proc. of IJCAI-97, pages 4-8. Morgan Kaufmann, 1997.
24. A. Srinivasan. PTE-Challenge, 1999. http://wwwxomlab.ox.ac.uk/oucl/ groups/machlearn/PTE,
25. th ICML., pages 574-578. Morgan Kaufmann, 1998.
26. J. Wnek and R.S. Michalski. Discovering representation space transformations for learning concept descriptions combining DNF and M-of-N rules. In T. Fawcett, ed., Workshop on Constructive Induction, ICML-94, 1994.
27. D.H. Wolpert and W.G. Macready. No free lunch theorems for search. Technical report, Santa Fe Institute, 1995.
28. S. Zilberstein. Resource-bounded reasoning in intelligent systems. Computing Surveys, 28(4), 1996.