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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Detalles Bibliográficos
Autores principales: Straat, Michiel, Abadi, Fthi, Göpfert, Christina, Hammer, Barbara, Biehl, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512337/
https://www.ncbi.nlm.nih.gov/pubmed/33265863
http://dx.doi.org/10.3390/e20100775

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