Detailed Information

Cited 2 time in webofscience Cited 2 time in scopus
Metadata Downloads

Superiority Demonstration of Variance-Considered Machines by Comparing Error Rate with Support Vector Machines

Full metadata record
DC Field Value Language
dc.contributor.authorYeom, Hong-Gi-
dc.contributor.authorPark, Seung-Min-
dc.contributor.authorPark, Junheong-
dc.contributor.authorSim, Kwee-Bo-
dc.date.available2019-05-29T13:33:28Z-
dc.date.issued2011-06-
dc.identifier.issn1598-6446-
dc.identifier.issn2005-4092-
dc.identifier.urihttps://scholarworks.bwise.kr/cau/handle/2019.sw.cau/21500-
dc.description.abstractTo improve the performance of classification algorithms, we proposed a new variance-considered machine (VCM) classification algorithm in a previous study. The study showed theoretically that VCMs have lower error probabilities than SVMs. The purpose of this paper is to experimentally demonstrate the superiority of VCMs. Therefore, we verified our proposal with several case experiments using data following a Gaussian distribution with different variances and prior probabilities. To estimate performance, the experiment for each case was executed 1000 times and the error rates were averaged for accuracy. The data of each experiment have different distances between means of data, and different ratios between training data and testing data. Thus, we proved that the error rate of VCMs is lower than the error rate of SVMs, although their performances were not similar in each case. Consequently, we expect that VCMs will be applied to a variety fields.-
dc.format.extent6-
dc.language영어-
dc.language.isoENG-
dc.publisherINST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS-
dc.titleSuperiority Demonstration of Variance-Considered Machines by Comparing Error Rate with Support Vector Machines-
dc.typeArticle-
dc.identifier.doi10.1007/s12555-011-0321-1-
dc.identifier.bibliographicCitationINTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.9, no.3, pp 595 - 600-
dc.identifier.kciidART001555959-
dc.description.isOpenAccessN-
dc.identifier.wosid000291189900022-
dc.identifier.scopusid2-s2.0-80052650446-
dc.citation.endPage600-
dc.citation.number3-
dc.citation.startPage595-
dc.citation.titleINTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS-
dc.citation.volume9-
dc.type.docTypeArticle-
dc.publisher.location대한민국-
dc.subject.keywordAuthorClassification algorithm-
dc.subject.keywordAuthoroptimal hyperplane-
dc.subject.keywordAuthorsupport vector machine-
dc.subject.keywordAuthorvariance-considered machine-
dc.relation.journalResearchAreaAutomation & Control Systems-
dc.relation.journalWebOfScienceCategoryAutomation & Control Systems-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
Files in This Item
There are no files associated with this item.
Appears in
Collections
College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE