标题：RVM and SVM for Classification in Transient Stability Assessment
作者：Duan Qing; Zhao Jian-guo; Ma Yan; Luo Ke
作者机构：[Duan Qing] Shandong Univ, Sch Elect Engn, Jinan, Peoples R China.; [Zhao Jian-guo] State Grid China, Technol Coll, Jinan, Peoples R China.; [Ma Y 更多
会议名称：Asia-Pacific Power and Energy Engineering Conference (APPEEC)
会议日期：MAR 28-31, 2010
来源：2010 ASIA-PACIFIC POWER AND ENERGY ENGINEERING CONFERENCE (APPEEC)
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.