Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Statistical and neural classifiers: an integrated approach to design
Statistical and neural classifiers: an integrated approach to design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Learning Changing Concepts by Exploiting the Structure of Change
Machine Learning
Results in statistical discriminant analysis: a review of the former Soviet union literature
Journal of Multivariate Analysis
Trainable fusion rules. I. Large sample size case
Neural Networks
Social Organization of Evolving Multiple Classifier System Functioning in Changing Environments
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Prediction of commodity prices in rapidly changing environments
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
The multi-agent system for prediction of financial time series
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Parameter learning from stochastic teachers and stochastic compulsive liars
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
Combining Time and Space Similarity for Small Size Learning under Concept Drift
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
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Many processes experience abrupt changes in their dynamics. This causes problems for some prediction algorithms which assume that the dynamics of the sequence to be predicted are constant, or at least only change slowly over time. In this paper the problem of predicting sequences with sudden changes in dynamics is considered. For a model of multivariate Gaussian data we derive expected generalization error of standard linear Fisher classifier in situation where after unexpected task change, the classification algorithm learns on a mixture of old and new data. We show both analytically and by an experiment that optimal length of learning sequence depends on complexity of the task, input dimensionality, on the power and periodicity of the changes. The proposed solution is to consider a collection of agents, in this case non-linear single layer perceptrons (agents), trained by a memetic like learning algorithm. The most successful agents are voting for predictions. A grouped structure of the agent population assists in obtaining favorable diversity in the agent population. Efficiency of socially organized evolving multi-agent system is demonstrated on an artificial problem.