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Online Machine Learning
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Author(s): Óscar Fontenla-Romero (University of A Coruña, Spain), Bertha Guijarro-Berdiñas (University of A Coruña, Spain), David Martinez-Rego (University of A Coruña, Spain), Beatriz Pérez-Sánchez (University of A Coruña, Spain)and Diego Peteiro-Barral (University of A Coruña, Spain)
Copyright: 2013
Pages: 28
Source title:
Efficiency and Scalability Methods for Computational Intellect
Source Author(s)/Editor(s): Boris Igelnik (BMI Research, Inc., USA)and Jacek M. Zurada (University of Louisville, USA)
DOI: 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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