Logical settings for concept-learning

Artificial Intelligence

Volumn 95, Issue 1, 1997, Pages 187-201

  De Raedt, Luc ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Computational learning theory; Concept learning; Inductive logic programming; Logic

Indexed keywords

COMPUTATIONAL COMPLEXITY; KNOWLEDGE ACQUISITION; MATHEMATICAL MODELS; MEMBERSHIP FUNCTIONS;

COMPUTATIONAL LEARNING THEORY; CONCEPT LEARNING; INDUCTIVE LOGIC PROGRAMMING; LOGIC;

LOGIC PROGRAMMING;

EID: 0031198976     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0004-3702(97)00041-6     Document Type: Article

References (31)

1. D. Angluin, M. Frazier and L. Pitt, Learning conjunctions of horn clauses, Machine Learning 9 (1992) 147-162.
2. D. Angluin, Learning propositional horn sentences with hints, Tech. Rept. YALEU/DCS/RR-590, Yale University, New Haven, CT (1987).
3. W.W. Cohen and D. Page, Polynomial learnability and inductive logic programming: methods and results, New Generation Comput. 13 (1995).
4. W.W. Cohen, PAC-learning non-recursive Prolog clauses, Artificial Intelligence 79 (1995) 1-38.
5. L. De Raedt and L. Dehaspe, Learning from satisfiability, Tech. Rept. KUL-CW, Department of Computer Science, Katholieke Universiteit Leuven, Belgium, to appear.
6. L. De Raedt and L. Dehaspe, Clausal discovery, Machine Learning 26 (1997) 99-146.
7. L. De Raedt and S. Džeroski, First-order jk-clausal theories are PAC-learnable, Artificial Intelligence 70 (1994) 375-392.
8. L. De Raedt and N. Lavrač, Multiple predicate learning in two inductive logic programming settings, J. Interest Group in Pure and Applied Logics 4 (2) (1996) 227-254.
9. L. De Raedt and W. Van Laer, Inductive constraint logic, in: Proceedings 5th Workshop on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence, Vol. 997 (Springer, Berlin, 1995).
10. L. De Raedt, Induction in logic, in: R.S. Michalski and J. Wnek, eds., Proceedings 3rd International Workshop on Multistrategy Learning (1996) 29-38.
11. D. Fensel, M. Zickwolff and M. Wiese, Are substitutions the better examples?, in: L. De Raed7t, ed., Proceedings 5th International Workshop on Inductive Logic Programming (1995).
12. P. Flach, A framework for inductive logic programming, in: S. Muggleton, ed., Inductive Logic Programming (Academic Press, New York, 1992).
13. P.A. Flach, Inductive logic programming and philosophy of science, in: S. Wrobel, ed., Proceedings 4th International Workshop on Inductive Logic Programming, GMD-Studien, Vol. 237 (Gesellschaft für Mathematik und Datenverarbeitung MBH, Sankt Augustin, Germany, 1994).
14. P. Flach, An inquiry concerning the logic of induction, Ph.D. Thesis, Institute for Language Technology and Artificial Intelligence, Tilburg University, Netherlands (1995).
15. M. Frazier and L. Pitt, Learning from entailment, in: Proceedings 9th International Conference on7 Machine Learning, Amherst, MA (1993).
16. M.R. Genesereth and N.J. Nilsson, Logical Foundations of Artificial Intelligence (Morgan Kaufmann, Los Altos, CA, 1987).
17. R. Greiner, A.J. Grove and A. Kogan, Exploiting the omission of irrelevant data, in: L. Saitta, ed., Proceedings 13th International Conference on Machine Learning (Morgan Kaufmann, Los Altos, CA, 1996) 207-215.
18. H. Ishizaka, H. Arimura and T. Shinohara, Finding tree patterns consistent with positive and negative examples with queries, in: S. Arikawa and K.P. Jantke, eds., Proceedings Joint International Workshop on Analogical and Inductive Inference and Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence (Springer, Berlin, 1994) 317-332.
19. M. Kearns and U. Vazzirani, An Introduction to Computational Learning Theory (MIT Press, Cambridge, MA, 1994).
20. J.U. Kietz and S. Dzeroski, Inductive logic programming and learnability, SIGART Bull. 5 (1) (1994) 22-32.
21. J.W. Lloyd, Foundations of Logic Programming (Springer, Berlin, 2nd ed., 1987).
22. T.M. Mitchell, Generalization as search, Artificial Intelligence 18 (1982) 203-226.
23. S. Miyano, A. Shinohara and T. Shinohara, Which classes of elementary formal systems are polynomial-time learnable?, in: S. Arikawa, A. Maruoka and T. Sato, eds., Proceedings 2nd Workshop on Algorithmic Learning Theory (1991).
24. S. Muggleton and L. De Raedt, Inductive logic programming: theory and methods, J. Logic Programming 19-20 (1994) 629-679.
25. S. Muggleton and C. Feng, Efficient induction of logic programs, in: Proceedings 1st Conference on Algorithmic Learning Theory (Ohmsma, Tokyo, Japan, 1990) 368-381.
26. Y. Mukouchi and S. Arikawa, Inductive inference machines that can refute hypothesis spaces, in: K.P. Jantke, S. Kobayashi, E. Tomita and T. Yokomori, eds., Proceedings 3rd Workshop on Algorithmic Learning Theory, Lecture Notes in Artificial Intelligence, Vol. 744 (Springer, Berlin, 1993) 123-136.
27. L. Pitt and L. Valiant, Computational limitations on learning from examples, J. ACM 35 (1988) 965-984.
28. L. Pitt and M. Warmuth, Prediction-preserving reducibility, J. Comput. System Sci. 41 (1990) 430-467.
29. J.R. Quinlan, Learning logical definitions from relations, Machine Learning 5 (1990) 239-266.
30. L. Valiant, A theory of the learnable, Comm. ACM 27 (1984) 1134-1142.
31. S. Wrobel and S. Džeroski, The ILP description learning problem: towards a general model-level definition of data mining in ILP, in: Proceedings Fachgruppentreffen Maschinelles Lernen (1995).