Generalized version of the support vector machine for binary classification problems: supporting hyperplane machine
This work offers a generalized framework for binary classification, but it appears incremental as it builds directly on the classic SVM method.
The authors proposed a generalized version of the support vector machine (SVM) for binary classification, called the supporting hyperplane machine, which extends SVM to arbitrary transformations, and they derived formulations and provided an implementation in Octave.
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.