Improving rule evaluation using multitask learning

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3194, Issue , 2004, Pages 252-269

  Reid, Mark D ^a  

^a UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DECISION THEORY; LEARNING ALGORITHMS; LOGIC PROGRAMMING; MULTITASKING; PROBLEM SOLVING; SET THEORY; ALGORITHMS; LEARNING SYSTEMS;

INDUCTIVE LOGIC PROGRAMMING (ILP); MULTITASK LEARNING; RULE LEARNING ALGORITHMS; VIRTUAL CONTINGENCY TABLE; LEARNING PERFORMANCE; RELATIONAL LEARNING; RULE DESCRIPTIONS; RULE EVALUATION; SECONDARY TASKS; TRAINING EXAMPLE;

LEARNING SYSTEMS; INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 22944461083     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-30109-7_20     Document Type: Conference Paper

References (28)

1. Srinivasan, A., King, R.D.: Feature construction with inductive logic programming: a study of quantative predictions of biological activity aided by structural attributes. Data Mining and Knowledge Discovery 3 (1999) 37-57
2. Caruana, R.: Multitask learning. Machine Learning 28 (1997) 41-75
3. Mitchell, T.M.: The need for biases in learning generalizations. Technical Report CBM-TR-117, Rutgers University, New Brunswick, New Jersey (1980)
4. Nédellec, C., Rouveirol, C., Adé, H., Bergadano, F., Tausend, B.: Declarative bias in ILP. In: Advances in ILP. Volume 32 of Frontiers in AI and Applications. IOS Press (1996) 82-103
5. Fürnkranz, J.: Separate-and-conquer rule learning. Artificial Intelligence Review 13 (1999) 3-54
6. Datta, P., Kibler, D.F.: Concept sharing: A means to improve multi-concept learning. In: Proc. of the 10th ICML. (1993) 89-96
7. Khan, K., Muggleton, S., Parson, R.: Repeat learning using predicate invention. In: Proc. of the 8th ILP, Springer (1998) 165-174
8. Morin, J.: Learning Relational Clichés with Contextual Generalization. PhD thesis, School of Information Technology and Engineering, University of Ottawa, Canada (1999)
9. Srinivasan, A.: ALEPH: A learning engine for proposing hypotheses. Prolog code (2001) http://www.comlab.ox.ac.uk/oucl/research/areas/machlearn/Aleph/.
10. Silver, D.: Selective Transfer of Neural network Task Knowledge. PhD thesis, Graduate Program in Computer Science, University of Western Ontario, London, Ontario, Canada (2000)
11. Good, I.J.: The Estimation of Probabilities: An Essay on Modern Bayesian Methods. MIT Press (1965)
12. Fürnkranz, J., Flach, P.A.: An analysis of rule evaluation metrics. In: Proc. of the 19th ICML, AAAI Press (2003) 202-209
13. Muggleton, S.H.: Inverse entailment and progol. New Generation Computing 13 (1995) 245-286
14. Srinivasan, A.: A study of two sampling methods for analysing large datasets with ILP. Data Mining and Knowledge Discovery 3 (1999) 95-123
15. De Raedt, L., Bruynhooghe, M.: Interactive concept-learning and constructive induction by analogy. Machine Learning 8 (1992) 107-150
16. Srinivasan, A., Muggleton, S., King, R.D., Sternberg, M.J.E.: Mutagenesis: ILP experiments in a non-determinate biological domain. In: Proc. of the 4th ILP. (1994)
17. Srinivasan, A., King, R.D., Muggleton, S., Sternberg, M.J.E.: Carcinogenesis predictions using ILP. In: Proc. of the 7th ILP. (1997) 273-287
18. Holte, R.C., Acker, L.E., Porter, B.W.: Concept learning and the problem of small disjuncts. In: Proc. of the 11th IJCAI. (1989) 813-818
19. Srinivasan, A.: Extracting context-sensitive models in inductive logic programming. Machine Learning 44 (2001) 301-324
20. Srinivasan, A.: Personal communication. Email regarding chemical data (2002)
21. Lavrač, N., Flach, P., Zupan, B.: Rule evaluation measures: A unifying view. In: Proc. of the 9th ILP, Springer (1999) 174-185
22. Vilalta, R., Oblinger, D.: A quantification of distance-bias between evaluation metrics in classification. In: Proc. of the 17th ICML. (2000) 1087-1094
23. Cestnik, B.: Estimating probabilities: A crucial task in machine learning. In: Proc. of the 9th European Conference on AI, Pitman (1990) 147-149
24. Baxter, J.: A model of inductive bias learning. Journal of Artificial Intelligence Research 12 (2000) 149-198
25. Heskes, T.: Empirical bayes for learning to learn. In: Proc. of the 17th ICML, Morgan Kaufmann (2000) 367-374
26. Cussens, J.: Using prior probabilities and density estimation for relational classification. In: Proc. of the 8th ILP, Springer (1998) 106-115
27. McCreath, E., Sharma, A.: LIME: A system for learning relations. In: Proc. of the ALT-98. (1998) 336-374
28. Lavrač, N., Flach, P.A.: An extended transformation approach to inductive logic programming. ACM Trans, on Computational Logic (TOCL) 2 (2001) 458-494