DocumentCode
2286683
Title
Neural network realization of support vector methods for pattern classification
Author
Tan, Ying ; Xia, Youshen ; Wang, Jun
Author_Institution
Dept. of Electron. Eng. & Inf. Sci., Univ. of Sci. & Technol. of China, Hefei, China
Volume
6
fYear
2000
fDate
2000
Firstpage
411
Abstract
We apply a recurrent neural network to support vector machine (SVM) training for pattern recognition. Specifically, a primal-dual neural network is exploited to solve the quadratic programming problem encountered in training SVMs. The properties of the network allow one to design SVMs without adjustable network parameters and give a better solution for ill-posed problems
Keywords
learning (artificial intelligence); pattern classification; quadratic programming; recurrent neural nets; learning; pattern classification; pattern recognition; quadratic programming; recurrent neural network; support vector machine; Automation; Error correction; Information science; Neural networks; Pattern classification; Pattern recognition; Quadratic programming; Recurrent neural networks; Support vector machine classification; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.859430
Filename
859430
Link To Document