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Abstract: A decision list is an ordered list of conjunctive rules (?). Inductive algorithms such as AQ and CN2 learn decision lists incrementally, one rule at a time. Such algorithms face the rule overlap problem --- the classification accuracy of the decision list depends on the overlap between the learned rules. Thus, even though the rules are learned in isolation, they can only be evaluated in concert. Existing algorithms solve this problem by adopting a greedy, iterative structure. Once a rule is... (Update)
Context of citations to this paper: More
.... style rule learners akin to RL; parallel matching would undoubtedly enable dramatic scaling for systems such as BruteDL (Segal Etzioni, 1994), where efficiency is a primary concern. Parallelizing some other generate andtest machine learning programs would be slightly...
...27 Alice (Isoft) 29] x x x x x x x x x x L 1 x x x S, G Bayesian Knowl. Disc. Open U. 48] x x L 1 x N Brute (Univ. ofWashington) [52] x x S 1 x x x N IntelligentMiner (IBM) 25] x x x x x x L R x x x S,GSIGKDD Explorations. 28 T D P F Ix O Sy Ig A OC SS Ex L...
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BibTeX entry: (Update)
Segal, R. and Etzioni, O. Learning decision lists using homogeneous rules. In Proceedings of the 12th National Conference on Artificial Intelligence, July 1994. http://citeseer.ist.psu.edu/segal94learning.html More
@inproceedings{ segal94learning,
author = "Richard Segal and Oren Etzioni",
title = "Learning Decision Lists Using Homogeneous Rules",
booktitle = "National Conference on Artificial Intelligence",
pages = "619-625",
year = "1994",
url = "citeseer.ist.psu.edu/segal94learning.html" }
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36 the quasi-minimal solution of the general covering problem (context) - Michalski - 1969
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22 Scientific Explanation and the Causal Structure of the World (context) - Salmon - 1984
21 Maximizing the predictive value of production rules (context) - Weiss, Galen et al. - 1990 ACM DBLP
15 Systematic search for categorical attribute-value data-drive.. - Webb - 1993
4 Learning decision trees (context) - Rivest - 1987
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