Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels. (August 2016)

• Record Type: Journal Article
• Authors: Utkin, Lev V.; Chekh, Anatoly I.; Zhuk, Yulia A.
• Is Part Of: Neural networks. Volume 80(2016)
• Page Start: 53
• Page End: 66
• Publication Date: 2016-08
• Subjects: Classification -- Support vector machine -- Interval-valued data -- Minimax strategy -- Linear programming -- Quadratic programming; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2016.04.005
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: Classification algorithms based on different forms of support vector machines (SVMs) for dealing with interval-valued training data are proposed in the paper. L 2 -norm and L ∞ -norm SVMs are used for constructing the algorithms. The main idea allowing us to represent the complex optimization problems as a set of simple linear or quadratic programming problems is to approximate the Gaussian kernel by the well-known triangular and Epanechnikov kernels. The minimax strategy is used to choose an optimal probability distribution from the set and to construct optimal separating functions. Numerical experiments illustrate the algorithms.