Self-organizing maps
Statistical Mechanics of Online Learning of Drifting Concepts: A Variational Approach
Machine Learning - Special issue on context sensitivity and concept drift
The handbook of brain theory and neural networks
On-line learning in neural networks
On-line learning in neural networks
A Bayesian approach to on-line learning
On-line learning in neural networks
Optimal perceptron learning: as online Bayesian approach
On-line learning in neural networks
Statistical Mechanics of Learning
Statistical Mechanics of Learning
Generalized relevance learning vector quantization
Neural Networks - New developments in self-organizing maps
Dynamics and Generalization Ability of LVQ Algorithms
The Journal of Machine Learning Research
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We introduce and discuss the application of statistical physics concepts in the context of on-line machine learning processes. The consideration of typical properties of very large systems allows to perfom averages over the randomness contained in the sequence of training data. It yields an exact mathematical description of the training dynamics in model scenarios. We present the basic concepts and results of the approach in terms of several examples, including the learning of linear separable rules, the training of multilayer neural networks, and Learning Vector Quantization.