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A review of adaptive online learning for artificial neural networks

Authors
  • Pérez-Sánchez, Beatriz1
  • Fontenla-Romero, Oscar1
  • Guijarro-Berdiñas, Bertha1
  • 1 University of A Coruña, Department of Computer Science, Faculty of Informatics, Campus de Elviña, A Coruña, 15071, Spain , A Coruña (Spain)
Type
Published Article
Journal
Artificial Intelligence Review
Publisher
Springer-Verlag
Publication Date
Oct 22, 2016
Volume
49
Issue
2
Pages
281–299
Identifiers
DOI: 10.1007/s10462-016-9526-2
Source
Springer Nature
Keywords
License
Yellow

Abstract

In real applications learning algorithms have to address several issues such as, huge amount of data, samples which arrive continuously and underlying data generation processes that evolve over time. Classical learning is not always appropriate to work in these environments since independent and indentically distributed data are assumed. Taking into account the requirements of the learning process, systems should be able to modify both their structures and their parameters. In this survey, our aim is to review the developed methodologies for adaptive learning with artificial neural networks, analyzing the strategies that have been traditionally applied over the years. We focus on sequential learning, the handling of the concept drift problem and the determination of the network structure. Despite the research in this field, there are currently no standard methods to deal with these environments and diverse issues remain an open problem.

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