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Online Machine Learning

Óscar Fontenla-Romero, Bertha Guijarro-Berdiñas, David Martinez-Rego, Beatriz Pérez-Sánchez, Diego Peteiro-Barral

Source Title: Efficiency and Scalability Methods for Computational Intellect

ISBN13: 9781466639423|ISBN10: 1466639423|EISBN13: 9781466639430

DOI: 10.4018/978-1-4666-3942-3.ch002

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MLA

Fontenla-Romero, Óscar, et al. "Online Machine Learning." Efficiency and Scalability Methods for Computational Intellect, edited by Boris Igelnik and Jacek M. Zurada, IGI Global, 2013, pp. 27-54. https://doi.org/10.4018/978-1-4666-3942-3.ch002

APA

Fontenla-Romero, Ó., Guijarro-Berdiñas, B., Martinez-Rego, D., Pérez-Sánchez, B., & Peteiro-Barral, D. (2013). Online Machine Learning. In B. Igelnik & J. Zurada (Eds.), Efficiency and Scalability Methods for Computational Intellect (pp. 27-54). IGI Global. https://doi.org/10.4018/978-1-4666-3942-3.ch002

Chicago

Fontenla-Romero, Óscar, et al. "Online Machine Learning." In Efficiency and Scalability Methods for Computational Intellect, edited by Boris Igelnik and Jacek M. Zurada, 27-54. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-3942-3.ch002

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Abstract

Machine Learning (ML) addresses the problem of adjusting those mathematical models which can accurately predict a characteristic of interest from a given phenomenon. They achieve this by extracting information from regularities contained in a data set. From its beginnings two visions have always coexisted in ML: batch and online learning. The former assumes full access to all data samples in order to adjust the model whilst the latter overcomes this limiting assumption thus expanding the applicability of ML. In this chapter, we review the general framework and methods of online learning since its inception are reviewed and its applicability in current application areas is explored.

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Online Machine Learning

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