Title : ( Support vector regression with random output variable and probabilistic constraints )
Authors: maryam abaszade , Sohrab Effati ,
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Abstract
Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variable with uniform probability function. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadratic optimization problem. The proposed method is illustrated by several simulated data and real datasets for both models (linear and nonlinear ) with probabilistic constraints.
Keywords
Probabilistic constraints; Support Vector Machine; Support Vector Regression; Quadratic programming; Probability function; Monte Carlo simulation
@article{paperid:1059201,
author = {Abaszade, Maryam and Effati, Sohrab},
title = {Support vector regression with random output variable and probabilistic constraints},
journal = {Iranian Journal of Fuzzy Systems},
year = {2016},
volume = {13},
number = {6},
month = {January},
issn = {1735-0654},
pages = {1--13},
numpages = {12},
keywords = {Probabilistic constraints; Support Vector Machine; Support Vector Regression; Quadratic programming; Probability function; Monte Carlo simulation},
}
%0 Journal Article
%T Support vector regression with random output variable and probabilistic constraints
%A Abaszade, Maryam
%A Effati, Sohrab
%J Iranian Journal of Fuzzy Systems
%@ 1735-0654
%D 2016
