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K. Duan, S. Keerthi and A. Poo, “Evaluation of Simple Performance Measures for Tuning SVM Hyperparameters,” Neurocomputing, Vol. 51, 2003, pp. 41-59. doi:10.1016/S0925-2312(02)00601-X

has been cited by the following article:

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

  AUTHORS: Pablo Rivas-Perea, Juan Cota-Ruiz, J. A. Perez Venzor, David Garcia Chaparro, Jose-Gerardo Rosiles

  KEYWORDS: Hyper-Parameter Estimation; Support Vector Regression; Machine Learning; Data Mining

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.1, February 22, 2013

  ABSTRACT: In this paper we study the problem of model selection for a linear programming-based support vector machine for regression. We propose generalized method that is based on a quasi-Newton method that uses a globalization strategy and an inexact computation of first order information. We explore the case of two-class, multi-class, and regression problems. Simulation results among standard datasets suggest that the algorithm achieves insignificant variability when measuring residual statistical properties.