Induction of Recursive Theories in the Normal ILP Setting: Issues and Solutions

1. Aha, D.W., Lapointe, S., Ling, C.X., Matwin, S.: Learning recursive relations with randomly selected small training sets. Proc. 11th Int. Conf. on Machine Learning, (1994) 12–18

   Google Scholar 

2. Apt, K.R.: Logic programming. In:van Leeuwen, J. (ed.): Handbook of Theoretical Computer Science, Vol. B. Elsevier, Amsterdam (1990) 493–574

   Google Scholar 

3. Boström, H.: Specialization of recursive predicates. In Lavrac, N., Wrobel, S. (eds.): Machine Learning ECML-95. Lecture Notes in Artificial Intelligence, Vol. 912. Springer-Verlag, Berlin(1995) 92–106

   Google Scholar 

4. Buntine, W.: Generalised subsumption and its applications to induction and redundancy. Artificial Intelligence, Vol. 36 (1988) 149–176

   Article  MATH  MathSciNet  Google Scholar 

5. Cameron-Jones, R.M., Quinlan, J.R.: Avoiding pitfalls when learning recursive theories. Proc. 12th Int. Joint Conf. on Artificial Intelligence, (1993) 1050–1055

   Google Scholar 

6. Ceri, S., Gottlob, G., Tanca, L.: What you always wanted to know about Datalog (and never dared to ask). IEEE Transactions on Knowledge and Data Engineering 1(1) (1989) 146–166

   Article  Google Scholar 

7. Cohen, W. W.: Learnability of restricted logic programs. In: Muggleton, S. (ed.): Proc. 3rd Int. Workshop on Inductive Logic Programming, (1993) 41–71

   Google Scholar 

8. DeRaedt, L.: Interactive Theory Revision. Academic Press, London (1992)

   Google Scholar 

9. DeRaedt, L., Lavrac, N., Dzeroski, S.: Multiple predicate learning. Proc. 13th Int. Joint Conf. on Artificial Intelligence, (1993) 1037–1042

   Google Scholar 

10. DeRaedt, L., Lavrac, N.: The many faces of inductive logic programming. In: Komorowski, J., Ras, Z.W. (eds.): Methodologies for Intelligent Systems. Lecture Notes in Artificial Intelligence, Vol. 689. Springer-Verlag, Berlin (1993) 435–449

    Google Scholar 

11. DeRaedt, L., Dehaspe, L.: Clausal discovery. Machine Learning 26(2/3), (1997) 99–146

    Google Scholar 

12. Esposito, F., Caggese, S., Malerba, D., Semeraro, G.: Classification in noisy domains by flexible matching. Proc. European Symposium on Intelligent Techniques, (1997) 45–49

    Google Scholar 

13. Esposito, F., Malerba, D., Lisi, F.A.: Machine Learning for Intelligent Processing of Printed Documents. In: Ras, Z.W., Skowron, A. (eds.): Journal of Intelligent Information Systems 16. Kluwer Academic Publishers (2000) 175–198

    Google Scholar 

14. Flach, P.: A framework for inductive logic programming. In: Muggleton, S. (ed.): Inductive Logic Programming, Vol. 38 of Apic Series. Academic Press, London (1992) 193–211

    Google Scholar 

15. Flener, P., Yilmaz, S.: Inductive Synthesis of Recursive Logic Programs: Achievements and Prospects. Journal of Logic Programming 41(2/3),Special Issue on Synthesis, Transformation and Analysis, (1999) 141–195

    Google Scholar 

16. Giordana, A., Saitta, L., Baroglio, C.: Learning simple recursive theories. In: Komorowski, J., Ras, Z.W. (eds.): Methodologies for Intelligent Systems. Lecture Notes in Artificial Intelligence, Vol. 689. Springer-Verlag, Berlin (1993) 425–434

    Google Scholar 

17. Idestam-Almquist, P.: Efficient induction of recursive definitions by structural analysis of saturations. In: DeRaedt, L. (ed.): Advances in Inductive Logic Programming. IOS Press, Amsterdam(1996) 192–205

    Google Scholar 

18. Jorge, A., Brazdil, P.: Architecture for iterative learning of recursive definitions. In: De Raedt, L. (ed.): Advances in Inductive Logic Programming. IOS Press, Amsterdam (1996) 206–218

    Google Scholar 

19. vanLeeuwen, J.: Graph Algorithms. In: van Leeuwen, J. (ed.): Handbook of Theoretical Computer Science, Vol. A. Elsevier, Amsterdam (1990) 525–631

    Google Scholar 

20. Malerba, D., Semeraro, G., Esposito, F.: A multistrategy approach to learning multiple dependent concepts. In: Nakhaeizadeh, G., Taylor, C. (eds.): Machine Learning and Statistics: The interface. John Wiley & Sons, New York (1997) 87–106

    Google Scholar 

21. Malerba, D., Esposito, F., Lisi, F.A.: Learning Recursive Theories with ATRE. In: Prade, H. (ed.), Proc. 13th Europ. Conf. on Artificial Intelligence. John Wiley & Sons, Chichester (1998) 435–439

    Google Scholar 

22. Mitchell, T.M.: Machine Learning. McGraw-Hill (1997)

    Google Scholar 

23. Mofizur, C.R., Numao, M.: Top-down induction of recursive programs from small number of sparse examples. In: De Raedt, L. (ed.): Advances in Inductive Logic Programming. IOS Press, Amsterdam (1996) 236–253

    Google Scholar 

24. Nagy, G., Seth, S.C., Stoddard, S.D.: A prototype document image analysis system for technical journals. IEEE Computer 25(7), (1992) 10–22

    Google Scholar 

25. Nienhuys-Cheng, S.-W., deWolf, R.: A complete method for program specialization based upon unfolding. Proc. 12th Europ. Conf. on Artificial Intelligence (1996) 438–442

    Google Scholar 

26. Plotkin, G.D.: A note on inductive generalization. In: Meltzer, B., Michie, D. (eds.): Machine Intelligence 5. Edinburgh University Press, Edinburgh (1970) 153–163

    Google Scholar 

27. Plotkin, G.D.: A further note on inductive generalization. In: Meltzer, B., Michie, D. (eds.): Machine Intelligence 6. Edinburgh University Press, Edinburgh (1971) 101–124

    Google Scholar 

28. Quinlan, J.R.: Learning Logical Definitions from Relations. Machine Learning 5 (1990) 239–266

    Google Scholar 

29. Rouveirol, C.: Flattening and saturation: Two representation changes for generalization. Machine Learning 14(2) (1994) 219–232

    Article  MATH  Google Scholar 

Download references