Metalearning: a survey of trends and technologies

Authors: Lemke, C., Budka, M. and Gabrys, B.

http://eprints.bournemouth.ac.uk/21017/

Journal: Artificial Intelligence Review

Volume: 44

Issue: 1

Pages: 117-130

eISSN: 1573-7462

ISSN: 0269-2821

DOI: 10.1007/s10462-013-9406-y

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. © 2013 The Author(s).

This data was imported from PubMed:

Authors: Lemke, C., Budka, M. and Gabrys, B.

http://eprints.bournemouth.ac.uk/21017/

Journal: Artif Intell Rev

Volume: 44

Issue: 1

Pages: 117-130

ISSN: 0269-2821

DOI: 10.1007/s10462-013-9406-y

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.

This data was imported from DBLP:

Authors: Lemke, C., Budka, M. and Gabrys, B.

http://eprints.bournemouth.ac.uk/21017/

Journal: Artif. Intell. Rev.

Volume: 44

Pages: 117-130

DOI: 10.1007/s10462-013-9406-y

This source preferred by Marcin Budka

This data was imported from Scopus:

Authors: Lemke, C., Budka, M. and Gabrys, B.

http://eprints.bournemouth.ac.uk/21017/

Journal: Artificial Intelligence Review

Volume: 44

Issue: 1

Pages: 117-130

eISSN: 1573-7462

ISSN: 0269-2821

DOI: 10.1007/s10462-013-9406-y

© 2013, The Author(s). 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.

This source preferred by Marcin Budka

This data was imported from Scopus:

Authors: Budka, M., Gabrys, B. and Lemke, C.

http://eprints.bournemouth.ac.uk/21017/

Journal: Artificial Intelligence Review

Pages: 1-14

eISSN: 1573-7462

ISSN: 0269-2821

DOI: 10.1007/s10462-013-9406-y

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. © 2013 The Author(s).

This data was imported from Web of Science (Lite):

Authors: Lemke, C., Budka, M. and Gabrys, B.

http://eprints.bournemouth.ac.uk/21017/

Journal: ARTIFICIAL INTELLIGENCE REVIEW

Volume: 44

Issue: 1

Pages: 117-130

eISSN: 1573-7462

ISSN: 0269-2821

DOI: 10.1007/s10462-013-9406-y

This data was imported from Europe PubMed Central:

Authors: Lemke, C., Budka, M. and Gabrys, B.

http://eprints.bournemouth.ac.uk/21017/

Journal: Artificial intelligence review

Volume: 44

Issue: 1

Pages: 117-130

eISSN: 1573-7462

ISSN: 0269-2821

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.

The data on this page was last updated at 04:42 on September 20, 2017.