A Simple, Fast, and Effective Rule Learner (1999)

by William W. Cohen , Yoram Singer
Venue:In Proceedings of the Sixteenth National Conference on Artificial Intelligence
Citations:93 - 3 self

Active Bibliography

13 Pruning decision trees and lists – Eibe Frank - 2000
564 A Short Introduction to Boosting – Yoav Freund, Robert E. Schapire - 1999
50 Theoretical Views of Boosting and Applications – Robert E. Schapire - 1999
1 Automatically Exploring Hypotheses about Fault Prediction: a Comparative Study of Inductive Logic Programming Methods – William W. Cohen, Premkumar T. Devanbu - 1998
114 Machine-Learning Research -- Four Current Directions – Thomas G. Dietterich
135 Separate-and-conquer rule learning – Johannes Fürnkranz - 1999
43 Pruning Algorithms for Rule Learning – Johannes Fürnkranz - 1997
78 Relational Learning Techniques for Natural Language Information Extraction – Mary Elaine Califf - 1998
Heuristic Rule Learning – Frederik Janssen - 2012
5 Hyper-rectangle-based discriminative data generalization and applications in data mining – Byron Ju Gao - 2007
515 An Efficient Boosting Algorithm for Combining Preferences – Raj Dharmarajan Iyer , Jr. - 1999
c ○ 2002 Kluwer Academic Publishers. Manufactured in The Netherlands. Boosting Methods for Regression ∗ – Nigel Duffy, David Helmbold, Jyrki Kivinen
15 Boosting Methods for Regression – Nigel Duffy, David Helmbold - 200
110 An introduction to boosting and leveraging – Ron Meir, Gunnar Rätsch - 2003
51 Using Correspondence Analysis to Combine Classifiers – Christopher J. Merz, Sal Stolfo - 1998
13 On the Boosting Pruning problem – Christino Tamon, Jie Xiang - 2000
247 Context-Sensitive Learning Methods for Text Categorization – William W. Cohen, Yoram Singer - 1996
35 Nearest neighbor classification from multiple feature subsets – Stephen D. Bay - 1999
1 Pruning Methods for Rule Learning Algorithms – Johannes Fürnkranz - 1994