Learning in the presence of concept drift and hidden contexts
Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching
Artificial Intelligence Review - Special issue on lazy learning
Machine Learning - Special issue on context sensitivity and concept drift
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Identifying suspicious URLs: an application of large-scale online learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
An ensemble approach for incremental learning in nonstationary environments
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Bayesian approach to the concept drift in the pattern recognition problems
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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The classical learning problem of the pattern recognition in a finite-dimensional linear space of real-valued features is studied under the conditions of a non-stationary universe. The training criterion of non-stationary pattern recognition is formulated as a generalization of the classical Support Vector Machine. The respective numerical algorithm has the computation complexity proportional to the length of the training time series.