A New Method for Classifying Random Variables Based on Support Vector Machine

Maryam Abaszade and Sohrab Effati ()
Additional contact information
Maryam Abaszade: Ferdowsi University of Mashhad
Sohrab Effati: Ferdowsi University of Mashhad

Journal of Classification, 2019, vol. 36, issue 1, No 9, 152-174

Abstract: 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 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s00357-018-9282-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jclass:v:36:y:2019:i:1:d:10.1007_s00357-018-9282-x

Ordering information: This journal article can be ordered from
http://www.springer. ... hods/journal/357/PS2

DOI: 10.1007/s00357-018-9282-x

Access Statistics for this article

Journal of Classification is currently edited by Douglas Steinley

More articles in Journal of Classification from Springer, The Classification Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().