EML: A Scalable, Transparent Meta-Learning Paradigm for Big Data Applications-Reference-Cited by-同舟云学术

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EML: A Scalable, Transparent Meta-Learning Paradigm for Big Data Applications

Published:2019 Issue: Volume: Page:35-59
ISSN:1868-4394
Container-title:Intelligent Systems Reference Library
language:
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Author:

Kamath Uday,Domeniconi Carlotta,Shehu Amarda,De Jong Kenneth

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-15939-9_3

Reference102 articles.

1. Anwar, F., Baker, S.M., Jabid, T., Hasan, M.M., Shoyaib, M., Khan, H., Walshe, R.: Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach. BMC Bioinform. 9, 414–421 (2008)

2. Bacardit, J., Llorà, X.: Large scale data mining using genetics-based machine learning. In: Proceedings of the GECCO-2012, pp. 1171–1196 (2012)

3. Barash, Y., Elidan, G., Friedman, N., Kaplan, T.: Modeling dependencies in protein-DNA binding sites. In: Istrail, S., Pevzner, P., Waterman, M. (eds.) International Conference on Research in Computational Molecular Biology (RECOMB), pp. 1–8. ACM Press, New York, NY (2003)

4. Batzoglou, S., Jaffe, D.B., Stanley, K., Butler, J., Gnerre, S., Mauceli, E., Berger, B., Mesirov, J.P., Lander, E.S.: ARACHNE: a whole-genome shotgun assembler. Genome Res. 12(1), 177–189 (2002)

5. Ben-Gal, I., Shani, A., Gohr, A., Grau, J., Arviv, S., Shmilovici, A., Posch, S., Grosse, I.: Identification of transcription factor binding sites with variable-order bayesian networks. Bioinformatics 21(11), 2657–2666 (2005)

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2. More Than Data Mining;Intelligent Systems Reference Library;2019

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