A statistical approach to rule learning

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 785-792

  Rückert, Ulrich ^a   Kramer, Stefan ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BIT ERROR RATE; COMPUTATIONAL COMPLEXITY; MATHEMATICAL MODELS; OPTIMIZATION; PROBLEM SOLVING; STATISTICAL METHODS;

CAPACITY CONTROL; COMPUTATIONAL HARDNESS; EFFICIENT COMPUTATION; ERROR RATE;

LEARNING SYSTEMS;

EID: 34250732316     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143943     Document Type: Conference Paper

References (8)

1. Cohen, W., & Singer, Y. (1999). A simple, fast, and effective rule learner. Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI99) (pp. 335-342). AAAI Press.
2. Frank, E., & Witten, I. H. (1998). Generating accurate rule sets without global optimization. Proc. 15th International Conf. on Machine Learning (pp. 144-151). Morgan Kaufmann.
3. Friedman, J. H., & Popescu, B. E. (2005). Predictive learning via rule ensembles (Technical Report). Stanford University.
4. Fürnkranz, J. (1999). Separate-and-conquer rule learning. Artificial Intelligence Review, 13, 3-54.
5. Rückert, U., & De Raedt, L. (2006). An experimental evaluation of comprehensibility in rule learning. Submitted.
6. Rückert, U., & Kramer, S. (2004). Towards tight bounds for rule learning. Machine Learning, Proceedings of the 21st International Conf.. ACM.
7. Weiss, S., & Indurkhya, N. (2000). Lightweight rule induction. Proc. 17th International Conf. on Machine Learning (pp. 1135-1142). Morgan Kaufmann.
8. Zhu, J., Rosset, S., Hastie, T., & Tibshirani, R. (2004). 1-norm support vector machines. NIPS. MIT Press.