title: Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels.
abstract: 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.
authors.affiliation: Peter the Great Saint-Petersburg Polytechnic University, Russia. Electronic address: lev.utkin@gmail.com.
authors.forename: Lev V
authors.initials: LV
authors.lastname: Utkin
authors.affiliation: Saint Petersburg State Electrotechnical University, Russia. Electronic address: anatoly.chekh@gmail.com.
authors.forename: Anatoly I
authors.initials: AI
authors.lastname: Chekh
authors.affiliation: ITMO University, Russia. Electronic address: zhuk_yua@mail.ru.
authors.forename: Yulia A
authors.initials: YA
authors.lastname: Zhuk
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D011336:Probability; D060388:Support Vector Machine
keywords: Classification; Interval-valued data; Linear programming; Minimax strategy; Quadratic programming; Support vector machine