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Linear support vector regression with linear constraints

Published:2021-06-24 Issue:7 Volume:110 Page:1939-1974
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Klopfenstein Quentin,Vaiter Samuel^ORCID

Funder

Agence Nationale de la Recherche

Conseil régional de Bourgogne-Franche-Comté

Institut National de la Santé et de la Recherche Médicale

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s10994-021-06018-2.pdf

Reference42 articles.

1. Abbas, A. R., Wolslegel, K., Seshasayee, D., Modrusan, Z., & Clark, H. F. (2009). Deconvolution of blood microarray data identifies cellular activation patterns in systemic lupus erythematosus. PLoS ONE, 4(7), 1–16.

2. Barlow, R. E., & Brunk, H. D. (1972). The isotonic regression problem and its dual. Journal of the American Statistical Association, 67(337), 140–147.

3. Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, ACM, New York, NY, USA, COLT ’92, pp 144–152.

4. Bro, R., & De Jong, S. (1997). A fast non-negativity-constrained least squares algorithm. Journal of Chemometrics, 11(5), 393–401.

5. Chang, C., & Lin, C. (2002). Training v-support vector regression: theory and algorithms. Neural Computation, 14(8), 1959–1977.

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