Applied Intelligence, ( ISI ), Volume (47), No (1), Year (2017-6) , Pages (1-14)

Title : ( Stochastic support vector regression with probabilistic constraints )

Authors: maryam abaszade , Sohrab Effati ,

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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.

Keywords

, Support vector machine·Support vector regression·Margin maximization·Mathematical expectation·Plug, in estimator·Monte Carlo
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@article{paperid:1063469,
author = {Abaszade, Maryam and Effati, Sohrab},
title = {Stochastic support vector regression with probabilistic constraints},
journal = {Applied Intelligence},
year = {2017},
volume = {47},
number = {1},
month = {June},
issn = {0924-669X},
pages = {1--14},
numpages = {13},
keywords = {Support vector machine·Support vector regression·Margin maximization·Mathematical expectation·Plug-in estimator·Monte Carlo simulation},
}

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%0 Journal Article
%T Stochastic support vector regression with probabilistic constraints
%A Abaszade, Maryam
%A Effati, Sohrab
%J Applied Intelligence
%@ 0924-669X
%D 2017

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