Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels

Classification algorithms based on different forms of support vector machines (SVMs) for dealing with interval-valued training data are proposed in the paper. L2-norm and L∞-norm SVMs are used for constructing the algorithms. The main idea allowing us to represent the complex optimization problems a...

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Bibliographic Details
Published inNeural networks Vol. 80; pp. 53 - 66
Main Authors Utkin, Lev V., Chekh, Anatoly I., Zhuk, Yulia A.
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 01.08.2016
Subjects
Algorithms
Approximation
Artificial Intelligence
Classification
Construction
Interval-valued data
Kernels
Linear programming
Minimax strategy
Optimization
Probability
Quadratic programming
Strategy
Support Vector Machine
Training
Interval-valued data
Minimax strategy
Linear programming
Support vector machine
Quadratic programming
Classification
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ISSN0893-6080
1879-2782
1879-2782
DOI10.1016/j.neunet.2016.04.005

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Summary:Classification algorithms based on different forms of support vector machines (SVMs) for dealing with interval-valued training data are proposed in the paper. L2-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.
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2016.04.005