Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
403780 | Neural Networks | 2016 | 14 Pages |
Abstract
Classification algorithms based on different forms of support vector machines (SVMs) for dealing with interval-valued training data are proposed in the paper. L2L2-norm and L∞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.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Lev V. Utkin, Anatoly I. Chekh, Yulia A. Zhuk,