标题:RETRACTED ARTICLE: RVM and SVM for classification in transient stability assessment
作者:Duan, Qing ;Zhao, Jian-Guo ;Ma, Yan ;Luo, Ke
通讯作者:Duan, Q
作者机构:[Duan, Qing ] School of Electric Engineering, Shandong University, Jinan, China;[Ma, Yan ] State Nuclear Electric Power Planning Design and Research, 更多
会议名称:Asia-Pacific Power and Energy Engineering Conference, APPEEC 2010
会议日期:28 March 2010 through 31 March 2010
来源:Asia-Pacific Power and Energy Engineering Conference, APPEEC
出版年:2010
DOI:10.1109/APPEEC.2010.5448612
关键词:Bayesian learning; Relevance vector machine; Support vector machine; Transient stability assessment
摘要:This paper introduces a general Bayesian framework for obtaining sparse solutions to classify predicting, and the practical model 'relevance vector machine' (RVM) by Michael E. Tipping, which is applied in electric system transient stability assessment (TSA). As a bran-new thought of probabilistic learning model, it offers the superior level of generalization accuracy and a number of additional advantages comparable with the popular and state-of-the-art 'support vector machine' (SVM). Utilize the advantages of the RVM, it can be applied in sorts of practical engineering fields and gain the special benefits. ©2010 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:2
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84867094431&doi=10.1109%2fAPPEEC.2010.5448612&partnerID=40&md5=80693a650ec7e1d84652d2c80856eaf9
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