Statistical Mechanics of On-Line Learning Under Concept Drift

Statistical Mechanics of On-Line Learning Under Concept Drift

We introduce a modeling 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 prototy...

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Bibliographic Details
Main Authors: Michiel Straat, Fthi Abadi, Christina Göpfert, Barbara Hammer, Michael Biehl
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:Entropy
Subjects:
concept drift
on-line learning
continual learning
neural networks
learning vector quantization
statistical physics of learning
Online Access:http://www.mdpi.com/1099-4300/20/10/775

Internet

http://www.mdpi.com/1099-4300/20/10/775

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