From Computational Learning Theory to Discovery Science (1999)

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by Osamu Watanabe
Citations:5 - 3 self

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11 Scaling up a Boosting-Based Learner via Adaptive Sampling – Carlos Domingo, Osamu Watanabe - 2000
110 An introduction to boosting and leveraging – Ron Meir, Gunnar Rätsch - 2003
19 MadaBoost: A Modification of AdaBoost – Carlos Domingo, Osamu Watanabe - 2000
564 A Short Introduction to Boosting – Yoav Freund, Robert E. Schapire - 1999
515 An Efficient Boosting Algorithm for Combining Preferences – Raj Dharmarajan Iyer , Jr. - 1999
1 OPTIMIZATION FOR SPARSE AND ACCURATE CLASSIFIERS – Noam Goldberg - 2010
2 A robust boosting algorithm – Richard Nock, Patrice Lefaucheur - 2002
15 Boosting Methods for Regression – Nigel Duffy, David Helmbold - 200
c ○ 2002 Kluwer Academic Publishers. Manufactured in The Netherlands. Boosting Methods for Regression ∗ – Nigel Duffy, David Helmbold, Jyrki Kivinen
89 On the Boosting Ability of Top-Down Decision Tree Learning Algorithms – Michael Kearns, Yishay Mansour - 1995
50 Theoretical Views of Boosting and Applications – Robert E. Schapire - 1999
69 Extracting Comprehensible Models from Trained Neural Networks – W. Craven - 1996
47 Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms – Carlos Domingo, Ricard Gavalda, Osamu Watanabe - 1999
Experimental Evaluation of an Adaptive Boosting By Filtering Algorithm – Carlos Domingo, Osamu Watanabe
3 Smooth boosting using an information-based criterion – Kohei Hatano - 2006
11 Simple Sampling Techniques for Discovery Science – Osamu Watanabe - 2000
6 Sequential Sampling Algorithms: Unified Analysis and Lower Bounds – Ricard Gavalda, Osamu Watanabe - 2001
Lower Bounds in . . . Learning Theory via Analytic Methods – Alexander Alexandrovich Sherstov - 2009
The Fourier Transform in Computational Learning Theory – Jennifer Sun - 1996