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Survey on Noise Estimation and Removal Methods through SVM

by Rakshita Pandya, Kshitij Pathak
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 9
Year of Publication: 2014
Authors: Rakshita Pandya, Kshitij Pathak
10.5120/15014-3297

Rakshita Pandya, Kshitij Pathak . Survey on Noise Estimation and Removal Methods through SVM. International Journal of Computer Applications. 86, 9 ( January 2014), 25-32. DOI=10.5120/15014-3297

@article{ 10.5120/15014-3297,
author = { Rakshita Pandya, Kshitij Pathak },
title = { Survey on Noise Estimation and Removal Methods through SVM },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 9 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number9/15014-3297/ },
doi = { 10.5120/15014-3297 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:03:47.124432+05:30
%A Rakshita Pandya
%A Kshitij Pathak
%T Survey on Noise Estimation and Removal Methods through SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 9
%P 25-32
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The Support vector machine is statistical learning method but it is also recognized as another approach to solve and simplify data classification. SVM have been discovered as one of the successful classification techniques for many areas and application and it works on different datasets and gives appropriate result. There is a noise or irrelevant data present in datasets which leads to poor result so to remove those meaningless data some approaches are introduced for better result. In this paper an introduction of SVM (Support Vector Machine) and various noise estimation and noise removal methods based on support vector machine is presented.

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Index Terms

Computer Science
Information Sciences

Keywords

SVM Machine learning Datasets Noise estimation noise Removal Filters.

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