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      Metalearning: a survey of trends and technologies

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          Abstract

          Metalearning attracted considerable interest in the machine learning community in the last years. Yet, some disagreement remains on what does or what does not constitute a metalearning problem and in which contexts the term is used in. This survey aims at giving an all-encompassing overview of the research directions pursued under the umbrella of metalearning, reconciling different definitions given in scientific literature, listing the choices involved when designing a metalearning system and identifying some of the future research challenges in this domain.

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          Stacked generalization

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            Neural Network for Pattern Recognition

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              The Algorithm Selection Problem

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                Author and article information

                Contributors
                christiane.lemke@gmail.com
                +44-1202-965795 , +44-1202-965314 , mbudka@gmail.com
                bgabrys@bournemouth.ac.uk
                Journal
                Artif Intell Rev
                Artif Intell Rev
                Artificial Intelligence Review
                Springer Netherlands (Dordrecht )
                0269-2821
                1573-7462
                20 July 2013
                20 July 2013
                2015
                : 44
                : 1
                : 117-130
                Affiliations
                [ ]Bournemouth University, Poole House, Talbot Campus, Fern Barrow, BH12 5BB Poole, UK
                [ ]Unister GmbH, Leipzig, Germany
                Article
                9406
                10.1007/s10462-013-9406-y
                4459543
                26069389
                cb961db2-e185-4826-9284-daef96c3954f
                © The Author(s) 2013

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

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                © Springer Science+Business Media Dordrecht 2015

                metalearning,metaknowledge extraction,life-long learning

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