Classifying random variables based on support vector machine and a neural network scheme

ABSTRACT

Support vector machine (SVM) is a supervised machine-learning method which can be used for both classification and regression models. In this paper, we introduce a new model of SVM which any of training samples containing inputs and outputs are considered the random variables with known probability functions. The SVM is first converted into equivalent quadratic programming (QP) formulations in linear and nonlinear cases. An artificial neural network for SVM learning is then proposed. The presented neural network framework guarantees to obtain the optimal solution of the SVM problem. The existence and convergence of the trajectories of the network are studied. The Lyapunov stability for the considered neural network is also shown. The efficiency of the proposed method is shown by four illustrative examples.

KEYWORDS:

• Neural network
• classification
• quadratic optimisation
• convergent
• stability
• probabilistic constraints

Disclosure statement

No potential conflict of interest was reported by the author(s).