Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An improved data stream summary: the count-min sketch and its applications
Journal of Algorithms
On-line learning for very large data sets: Research Articles
Applied Stochastic Models in Business and Industry - Statistical Learning
Prediction, Learning, and Games
Prediction, Learning, and Games
On the role of tracking in stationary environments
Proceedings of the 24th international conference on Machine learning
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Dataset Shift in Machine Learning
Dataset Shift in Machine Learning
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After a long period of neglect, on-line learning is reemerging as an important topic in machine learning. On one hand, this is due to new applications involving data flows, the detection of, or adaption to, changing conditions and long-life learning. On the other hand, it is now apparent that the current statistical theory of learning, based on the independent and stationary distribution assumption, has reached its limits and must be completed or superseded to account for sequencing effects, and more generally, for the information carried by the evolution of the data generation process. This chapter first presents the current, still predominant paradigm. It then underlines the deviations to this framework introduced by new on-line learning settings, and the associated challenges that they raise both for devising novel algorithms and for developing a satisfactory new theory of learning. It concludes with a brief description of a new learning concept, called tracking, which may hint as to what could come off as algorithms and theoretical questions from looking anew to this allpervading situation: never to stop learning.