Mining chemical compound structure data using inductive logic programming

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3430 LNAI, Issue , 2005, Pages 92-111

  Nattee, Cholwich ^a   Sinthupinyo, Sukree ^a   Numao, Masayuki ^a   Okada, Takashi ^b  

^a OSAKA UNIVERSITY   (Japan)

^b KWANSEI GAKUIN UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; DATABASE SYSTEMS; KNOWLEDGE BASED SYSTEMS; LOGIC PROGRAMMING;

ANTAGONIST COMPOUNDS; INDUCTIVE LOGIC PROGRAMMING; MINING CHEMICAL COMPOUND;

DATA MINING;

EID: 26844443145     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11423270_6     Document Type: Conference Paper

References (15)

1. Quinlan, J.R.: Learning logical definitions from relations. Machine Learning 5 (1990) 239-266
2. Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89 (1997) 31-71
3. Wang, J., Zucker, J.D.: Solving the multiple-instance problem: A lazy learning approach. In: Proc. 17th International Conf. on Machine Learning, Morgan Kaufmann, San Francisco, CA (2000) 1119-1125
4. Chevaleyre, Y., Zucker, J.D.: A framework for learning rules from multiple instance data. In: Proc. 12th European Conf. on Machine Learning. Volume 2167 of LNCS., Springer (2001) 49-60
5. Gärtner, T., Flach, P.A., Kowalczyk, A., Smola, A.J.: Multi-instance kernels. In: Proc. 19th International Conf. on Machine Learning, Morgan Kaufmann (2002) 179-186
6. Maron, O., Lozano-Pérez, T.: A framework for multiple-instance learning. In Jordan, M.I., Kearns, M.J., Solla, S.A., eds.: Advances in Neural Information Processing Systems. Volume 10., The MIT Press (1998)
7. Srinivasan, A., Muggleton, S., King, R., Sternberg, M.: Mutagenesis: ILP experiments in a non-determinate biological domain. In Wrobel, S., ed.: Proc. 4th International Workshop on Inductive Logic Programming. Volume 237., Gesellschaft für Mathematik und Datenverarbeitung MBH (1994) 217-232
8. Srinivasan, A.: The Aleph manual (2001) http://web.comlab.ox.ac.uk/oucl/-research/areas/machleam/Aleph/.
9. Selman, B., Levesque, H.J., Mitchell, D.: A new method for solving hard satisfiability problems. In: Proceedings 10th National Conference on Artificial Intelligence. (1992) 440-446
10. King, R.D., Sternberg, M.J.E., Srinivasan, A.: Relating chemical activity to structure: An examination of ILP successes. New Generation Computing 13 (1995) 411-433
11. Srinivasan, A., Muggleton, S., Sternberg, M.J.E., King, R.D.: Theories for mutagenicity: A study in first-order and feature-based induction. Artificial Intelligence 85 (1996) 277-299
12. Weidmann, N., Frank, E., Pfahringer, B.: A two-level learning method for generalized multi-instance problems. In: Proceedings of the European Conference on Machine Learning (ECML-2003). (2003) 468-479
13. McGovern, A., Jensen, D.: Identifying predictive structures in relational data using multiple instance learning. In: Proceedings of the 20th International Conference on Machine Learning (ICML-2003). (2003)
14. Chevaleyre, Y., Zucker, J.D.: Solving multiple-instance and multiple-part learning problems with decision trees and decision rules: Application to the mutagenesis problem. Technical report, LIP6-CNRS, University Paris VI (2000)
15. Zucker, J.D.: Solving multiple-instance and multiple-part learning problems with decision trees and rule sets, application to the mutagenesis problem. In: Proceedings of Canadian Conference on AI 2001. (2001) 204-214