DocumentCode
1986095
Title
Application of support vector machine nonlinear classifier to fault diagnoses
Author
Yan, Weiwu ; Shao, Huihe
Author_Institution
Dept. of Autom., Shanghai Jiao Tong Univ., China
Volume
4
fYear
2002
fDate
2002
Firstpage
2697
Abstract
Support vector machine (SVM) is a novel machine learning method based on statistical learning theory. SVM is a powerful tool for solving problems with small samples, nonlinearities and local minima, and is of excellent performance in classification. In the paper, the SVM nonlinear classification algorithm is reviewed. The SVM nonlinear classifier is applied to deal with fault diagnosis. SVM is easy to implement for fault diagnosis. Effective results are obtained of using the SVM for fault diagnosis.
Keywords
fault diagnosis; learning automata; neural nets; pattern classification; statistical analysis; fault diagnosis; machine learning; nonlinear classifier; pattern classification; statistical learning; support vector machine; Automation; Classification algorithms; Fault diagnosis; Kernel; Lagrangian functions; Learning systems; Signal processing algorithms; Statistical learning; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2002. Proceedings of the 4th World Congress on
Print_ISBN
0-7803-7268-9
Type
conf
DOI
10.1109/WCICA.2002.1020004
Filename
1020004
Link To Document