Title :
Notice of Retraction
RVM and SVM for Classification in Transient Stability Assessment
Author :
Duan Qing ; Zhao Jian-guo ; Ma Yan ; Luo Ke
Author_Institution :
Sch. of Electr. Eng., Shandong Univ., Jinan, China
Abstract :
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
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.
Keywords :
Bayes methods; power engineering computing; power system transient stability; support vector machines; Bayesian framework; SVM; electric system transient stability assessment; probabilistic learning model; relevance vector machine; support vector machine; Arithmetic; Bayesian methods; Kernel; Machine learning; Power engineering and energy; Predictive models; Space technology; Stability; Support vector machine classification; Support vector machines;
Conference_Titel :
Power and Energy Engineering Conference (APPEEC), 2010 Asia-Pacific
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4812-8
DOI :
10.1109/APPEEC.2010.5448612
