The weighted majority algorithm
Information and Computation
Boosting a weak learning algorithm by majority
Information and Computation
On-line prediction and conversion strategies
Machine Learning
Journal of the ACM (JACM)
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
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
An Adaptive Version of the Boost by Majority Algorithm
Machine Learning
An Adaptive Version of the Boost by Majority Algorithm
Machine Learning
Potential-Based Algorithms in On-Line Prediction and Game Theory
Machine Learning
Potential-Based Algorithms in Online Prediction and Game Theory
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Learning with Continuous Experts Using Drifting Games
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Prototype classification: Insights from machine learning
Neural Computation
Monte Carlo theory as an explanation of bagging and boosting
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Learning with continuous experts using drifting games
Theoretical Computer Science
A theory of multiclass boosting
The Journal of Machine Learning Research
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We introduce and study a general, abstract game played between two players called the shepherd and the adversary. The game is played in a series of rounds using a finite set of “chips” which are moved about in {\bb R}^n. On each round, the shepherd assigns a desired direction of movement and an importance weight to each of the chips. The adversary then moves the chips in any way that need only be weakly correlated with the desired directions assigned by the shepherd. The shepherd's goal is to cause the chips to be moved to low-loss positions, where the loss of each chip at its final position is measured by a given loss function.We present a shepherd algorithm for this game and prove an upper bound on its performance. We also prove a lower bound showing that the algorithm is essentially optimal for a large number of chips. We discuss computational methods for efficiently implementing our algorithm.We show that our general drifting-game algorithm subsumes some well studied boosting and on-line learning algorithms whose analyses follow as easy corollaries of our general result.