Title : ( A New Method for Classifying Random Variables Based on Support Vector Machine )
Authors: maryam abaszade , Sohrab Effati ,
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Abstract
In this paper, a new version of Support Vector Machine (SVM) is proposed which any of training samples are considered the random variables. Hence, in order to achieve robustness, the constraint in SVM must be replaced with probability of constraint. In this new model by applying the nonparametric statistical methods, we obtain the optimal separating hyperplane by solving a quadratic optimization problem. Afterwards, we present the least squares model of our proposed method. The efficiency of our proposed method is shown by several examples for both cases (linear and nonlinear) with probabilistic constraints.
Keywords
, Probabilistic constraints, Support Vector Machine, Least squares Support Vector Machine, Mathematical expectation, Plug-in estimator, Monte Carlo simulation.
@article{paperid:1067039,
author = {Abaszade, Maryam and Effati, Sohrab},
title = {A New Method for Classifying Random Variables Based on Support Vector Machine},
journal = {Journal of Classification},
year = {2019},
volume = {36},
number = {1},
month = {April},
issn = {0176-4268},
pages = {152--174},
numpages = {22},
keywords = {Probabilistic constraints; Support Vector Machine; Least squares Support Vector Machine; Mathematical expectation; Plug-in estimator; Monte Carlo simulation.},
}
%0 Journal Article
%T A New Method for Classifying Random Variables Based on Support Vector Machine
%A Abaszade, Maryam
%A Effati, Sohrab
%J Journal of Classification
%@ 0176-4268
%D 2019
