DocumentCode :
1562926
Title :
Performance analysis and comparison of neural networks and support vector machines classifier
Author :
Zheng, Enhui ; Li, Ping ; Song, Zhihuan
Author_Institution :
Inst. of Ind. Process Control, Zhejiang Univ., Hangzhou, China
Volume :
5
fYear :
2004
Firstpage :
4232
Abstract :
The theory foundation and classification algorithm of neural networks (NN) and support vector machines (SVM) are researched and compared from their conceptual constructs to basic mathematical reasons, on the basis of which the SVM classification system and the NN classification system are constructed respectively. The performances of the two classification systems are tested on two sets of benchmark data, and the SVM classification system shows better performance in binary classification tasks.
Keywords :
learning (artificial intelligence); minimisation; neural nets; pattern classification; support vector machines; SVM classification system; binary classification tasks; classification algorithm; learning algorithm; mathematical reasons; minimization; neural network classification system; performance analysis; support vector machines; Least squares approximation; Neural networks; Pattern recognition; Performance analysis; Risk management; Statistical learning; Support vector machine classification; Support vector machines; System testing; Virtual colonoscopy;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
Type :
conf
DOI :
10.1109/WCICA.2004.1342308
Filename :
1342308
Link To Document :
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