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A review of adaptive online learning for artificial neural networks

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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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Acknowledgments

The authors would like to thank support for this work from the Xunta de Galicia (Grant code GRC2014/035) and the Secretaría de Estado de Investigación of the Spanish Government (Grant code TIN2015-65069), all partially supported by the European Union ERDF funds.

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  1. Department of Computer Science, Faculty of Informatics, University of A Coruña, Campus de Elviña, 15071, A Coruña, Spain

    Beatriz Pérez-Sánchez, Oscar Fontenla-Romero & Bertha Guijarro-Berdiñas

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  1. Beatriz Pérez-Sánchez
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Correspondence to Beatriz Pérez-Sánchez.

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Pérez-Sánchez, B., Fontenla-Romero, O. & Guijarro-Berdiñas, B. A review of adaptive online learning for artificial neural networks. Artif Intell Rev 49, 281–299 (2018). https://doi.org/10.1007/s10462-016-9526-2

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