Preprint Article Version 1 This version is not peer-reviewed

Statistical Mechanics of On-Line Learning Under Concept Drift

Version 1 : Received: 5 September 2018 / Approved: 5 September 2018 / Online: 5 September 2018 (16:27:10 CEST)

A peer-reviewed article of this Preprint also exists.

Straat, M.; Abadi, F.; Göpfert, C.; Hammer, B.; Biehl, M. Statistical Mechanics of On-Line Learning Under Concept Drift. Entropy 2018, 20, 775. Straat, M.; Abadi, F.; Göpfert, C.; Hammer, B.; Biehl, M. Statistical Mechanics of On-Line Learning Under Concept Drift. Entropy 2018, 20, 775.

Journal reference: Entropy 2018, 20, 775
DOI: 10.3390/e20100775

Abstract

We introduce a modelling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e. the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.

Subject Areas

neural networks; statistical physics of learning; on-line learning; concept drift; continual learning; learning vector quantization;

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