LP-SVR Model Selection Using an Inexact Globalized Quasi-Newton Strategy

Journal of Intelligent Learning Systems and Applications

ISSN Print: 2150-8402
ISSN Online: 2150-8410
www.scirp.net/journal/jilsa
E-mail: [email protected]

Citations

"LP-SVR Model Selection Using an Inexact Globalized Quasi-Newton Strategy"
written by Pablo Rivas-Perea, Juan Cota-Ruiz, J. A. Perez Venzor, David Garcia Chaparro, Jose-Gerardo Rosiles,
published by Journal of Intelligent Learning Systems and Applications, Vol.5 No.1, 2013

has been cited by the following article(s):

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[2]	A Proof of Sparseness, Optimality, and Convergence of an LP-SVR 2020 International Conference on Computational Science and Computational Intelligence (CSCI), 2020 DOI:10.1109/CSCI51800.2020.00083

[3]	Prediction of asphaltene precipitation using support vector regression tuned with genetic algorithms Petroleum, 2016 DOI:10.1016/j.petlm.2016.05.006

[4]	An algorithm for training a large scale support vector machine for regression based on linear programming and decomposition methods Pattern Recognition Letters, 2013 DOI:10.1016/j.patrec.2012.10.026

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