Data complexity meta-features for regression problems-Reference-Cited by-同舟云学术

Data complexity meta-features for regression problems

Published:2017-12-21 Issue:1 Volume:107 Page:209-246
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Lorena Ana C.^ORCID,Maciel Aron I.,de Miranda Péricles B. C.,Costa Ivan G.,Prudêncio Ricardo B. C.

Funder

Fundação de Amparo à Pesquisa do Estado de São Paulo

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Deutscher Akademischer Austauschdienst

IZKF Aachen

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s10994-017-5681-1/fulltext.html

Reference28 articles.

1. Amasyali, M., & Erson, O. (2009). A study of meta learning for regression. Tech. rep. ECE Technical Reports 386, Purdue University.

2. Armstrong, J. S. (2012). Illusions in regression analysis. International Journal of Forecasting, 28(3), 689–694.

3. Bache, K., & Lichman, M. (2013). UCI machine learning repository. http://archive.ics.uci.edu/ml , University of California, Irvine, School of Information and Computer Sciences.

4. Basak, D., Pal, S., & Patranabis, D. C. (2007). Support vector regression. Neural Information Processing-Letters and Reviews, 11(10), 203–224.

5. Brazdil, P., Giraud-Carrier, C., Soares, C., & Vilalta, R. (2008). Meta-learning: Applications to data mining. New York: Springer Science and Business Media.

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