Applied Intelligence, ( ISI ), Volume (48), No (1), Year (2018-1) , Pages (243-256)
Title : ( Stochastic support vector regression with probabilistic constraints )
Authors: maryam abaszade , Sohrab Effati ,Access to full-text not allowed by authors
Abstract
Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, we introduce a novel model of SVR in which any training samples containing inputs and outputs are considered the random variables with known or unknown distribution functions. Constraints occurrence have a probability density function which helps to obtain maximum margin and achieve robustness. The optimal hyperplane regression can be obtained by solving a quadratic optimization problem. The proposed method is illustrated by several experiments including artificial data sets and real-world benchmark data sets.