Argument based machine learning

Artificial Intelligence

Volumn 171, Issue 10-15, 2007, Pages 922-937

  Možina, Martin ^a   Žabkar, Jure ^a   Bratko, Ivan ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

Author keywords

Argumentation; Background knowledge; Knowledge intensive learning; Learning through arguments; Machine learning

Indexed keywords

COMBINATORIAL OPTIMIZATION; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS;

BACKGROUND KNOWLEDGE; KNOWLEDGE INTENSIVE LEARNING; LEARNING THROUGH ARGUMENTS;

LEARNING SYSTEMS;

EID: 34548011440     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2007.04.007     Document Type: Article

References (23)

1. Ashley K.D., and Rissland E.L. Law, learning and representation. Artificial Intelligence 150 (2003) 17-58
2. I. Bratko, M. Možina, Argumentation and machine learning, in: Deliverable 2.1 for the ASPIC project, 2004
3. S. Brüninghaus, K.D. Ashley, Predicting the outcome of case-based legal arguments, in: G. Sartor (Ed.), Proceedings of the 9th International Conference on Artificial Intelligence and Law (ICAIL), Edinburgh, United Kingdom, June 2003, pp. 233-242
4. B. Cestnik, Estimating probabilities: A crucial task in machine learning, in: Proceedings of the Ninth European Conference on Artificial Intelligence, 1990, pp. 147-149
5. P. Clark, Representing arguments as background knowledge for constraining generalisation, in: D. Sleeman (Ed.), Third European Working Session on Learning, October 1988
6. P. Clark, R. Boswell, Rule induction with CN2: Some recent improvements, in: Machine Learning-Proceedings of the Fifth European Conference (EWSL-91), Berlin, 1991, pp. 151-163
7. Clark P., and Niblett T. The CN2 induction algorithm. Machine Learning Journal 4 3 (1989) 261-283
8. J. Demšar, B. Zupan, Orange: From experimental machine learning to interactive data mining, White Paper, http://www.ailab.si/orange, Faculty of Computer and Information Science, University of Ljubljana, 2004
9. Domingos P. The role of Occam's razor in knowledge discovery. Data Mining and Knowledge Discovery 3 4 (1999) 409-425
10. Džeroski S., Cestnik B., and Petrovski I. Using the m-estimate in rule induction. CIT J. Comput. Inf. Technol. 1 (1993) 37-46
11. S.A. Gomez, C.I. Chesnevar, Integrating defeasible argumentation and machine learning techniques, Technical report, Universidad Nacional del Sur, 2004
12. Gomez S.A., and Chesnevar C.I. Integrating defeasible argumentation with fuzzy art neural networks for pattern classification. Journal of Computer Science and Technology 4 1 (April 2004) 45-51
13. Jensen D.D., and Cohen P.R. Multiple comparisons in induction algorithms. Machine Learning 38 3 (March 2000) 309-338
14. Možina M., Demšar J., Žabkar J., and Bratko I. Why is rule learning optimistic and how to correct it. In: Fuernkranz J., Scheffer T., and Spiliopoulou M. (Eds). Proceedings of 17th European Conference on Machine Learning (ECML 2006). Berlin (2006), Springer-Verlag 330-340
15. Možina M., Žabkar J., Bench-Capon T., and Bratko I. Argument based machine learning applied to law. Artificial Intelligence and Law 13 1 (2006) 53-73
16. Možina M., Žabkar J., and Bratko I. Argument based rule learning. Proceedings of 17th European Conference on Artificial Intelligence (ECAI 2006). Riva Del Garda, Italy (2006), IOS Press
17. M. Možina, J. Žabkar, I. Bratko, D3.4: Implementation of and experiments with abml and mlba, ASPIC Deliverable D3.4, 2006
18. P.M. Murphy, D.W. Aha, UCI repository of machine learning databases, http://www.ics.uci.edu/~mlearn/mlrepository.html, Irvine, CA: University of California, Department of Information and Computer Science, 1994
19. Pazzani M., Mani S., and Shankle W.R. Beyond concise and colorful: Learning intelligible rules. Third International Conference on Knowledge Discovery and Data Mining. Newport Beach, CA (1997), AAAI Press 235-238
20. Pazzani M.J. Influence of prior knowledge on concept acquisition: Experimental and computational results. Journal of Experimental Psychology: Learning, Memory and Cognition 17 (1991) 416-432
21. Prakken H., and Vreeswijk G. Logics for defeasible argumentation. Handbook of Philosophical Logic. second ed. vol. 4 (2002), Kluwer Academic Publishers, Dordrecht 218-319
22. Rousseauw J., du Plessis J., Benade A., Jordann P., Kotze J., Jooste P., and Ferreira J. Coronary risk factor screening in three rural communities. South African Medical Journal 64 (1983) 430-436
23. J. Žabkar, M. Možina, J. Videčnik, I. Bratko, Argument based machine learning in a medical domain, in: E.P. Dunne, T.J.M. Bench-Capon (Eds.), Proceedings of Computational Models of Argument (COMMA), 2006, pp. 59-70