Communications of the ACM
The Strength of Weak Learnability
Machine Learning
Cryptographic limitations on learning Boolean formulae and finite automata
Journal of the ACM (JACM)
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Boosting a weak learning algorithm by majority
Information and Computation
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Additive models, boosting, and inference for generalized divergences
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Boosting as entropy projection
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Prediction games and arcing algorithms
Neural Computation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Logistic Regression, AdaBoost and Bregman Distances
Machine Learning
Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Incorporating Prior Knowledge into Boosting
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ATTac-2001: A Learning, Autonomous Bidding Agent
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
| Hi-index | 0.00 |
Boosting is a general method of generating many simple classification rules and combining them into a single, highly accurate rule. This paper reviews the AdaBoost boosting algorithm and some of its underlying theory, and then looks at some of the challenges of applying AdaBoost to bidding in complicated auctions and to human-computer spoken-dialogues systems.