Generalized version of the support vector machine for binary classification problems: supporting hyperplane machine
Machine Learning
2014-04-16 v2 Machine Learning
Abstract
In this paper there is proposed a generalized version of the SVM for binary classification problems in the case of using an arbitrary transformation x -> y. An approach similar to the classic SVM method is used. The problem is widely explained. Various formulations of primal and dual problems are proposed. For one of the most important cases the formulae are derived in detail. A simple computational example is demonstrated. The algorithm and its implementation is presented in Octave language.
Keywords
Cite
@article{arxiv.1404.3415,
title = {Generalized version of the support vector machine for binary classification problems: supporting hyperplane machine},
author = {E. G. Abramov and A. B. Komissarov and D. A. Kornyakov},
journal= {arXiv preprint arXiv:1404.3415},
year = {2014}
}
Comments
22 pages with 3 figures, 1 Octave script