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 :
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