COLT '90 Proceedings of the third annual workshop on Computational learning theory
The weighted majority algorithm
Information and Computation
Machine Learning - Special issue on inductive transfer
Machine Learning - Special issue on context sensitivity and concept drift
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Learning with Support Vector Machines
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Recognizing Probabilistic Opponent Movement Models
RoboCup 2001: Robot Soccer World Cup V
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Online learning of linear classifiers
Advanced lectures on machine learning
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
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Many learning tasks in adversarial domains tend to be highly dependent on the opponent. Predefined strategies optimized for play against a specific opponent are not likely to succeed when employed against another opponent. Learning a strategy for each new opponent from scratch, though, is inefficient as one is likely to encounter the same or similar opponents again. We call this particular variant of inductive transfer a concept recall problem. We present an extension to AdaBoost called ExpBoost that is especially designed for such a sequential learning tasks. It automatically balances between an ensemble of experts each trained on one known opponent and learning the concept of the new opponent. We present and compare results of Exp-Boost and other algorithms on both synthetic data and in a simulated robot soccer task. ExpBoost can rapidly adjust to new concepts and achieve performance comparable to a classifier trained exclusively on a particular opponent with far more data.