The Strength of Weak Learnability
Machine Learning
Boosting a weak learning algorithm by majority
Information and Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An adaptive version of the boost by majority algorithm
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
Data Mining and Knowledge Discovery
Scaling Up a Boosting-Based Learner via Adaptive Sampling
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
MadaBoost: A Modification of AdaBoost
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Logistic Regression, AdaBoost and Bregman Distances
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Sequential Sampling Techniques for Algorithmic Learning Theory
ALT '00 Proceedings of the 11th International Conference on Algorithmic Learning Theory
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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We discuss algorithmic aspects of boosting techniques, such as Majority Vote Boosting [Fre95], AdaBoost [FS97], and MadaBoost [DW00a]. Considering a situation where we are given a huge amount of examples and asked to find some rule for explaining these example data, we show some reasonable algorithmic approaches for dealing with such a huge dataset by boosting techniques. Through this example, we explain how to use and how to implement "adaptivity" for scaling-up existing algorithms.