  We present a modelling framework for the investigation of prototype-based
classifiers in non-stationary environments. Specifically, we study Learning
Vector Quantization (LVQ) systems trained from a stream of high-dimensional,
clustered data.We consider standard winner-takes-all updates known as LVQ1.
Statistical properties of the input data change on the time scale defined by
the training process. We apply analytical methods borrowed from statistical
physics which have been used earlier for the exact description of learning in
stationary environments. The suggested framework facilitates the computation of
learning curves in the presence of virtual and real concept drift. Here we
focus on timedependent class bias in the training data. First results
demonstrate that, while basic LVQ algorithms are suitable for the training in
non-stationary environments, weight decay as an explicit mechanism of
forgetting does not improve the performance under the considered drift
processes.
