Neural network realization of support vector methods for pattern classification

Ying Tan; Youshen Xia; Jun Wang

doi:10.1109/IJCNN.2000.859430

Neural network realization of support vector methods for pattern classification

Ying Tan, Youshen Xia, Jun Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

31 Citations (Scopus)

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.

Original language	English
Title of host publication	Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000)
Subtitle of host publication	Neural Computing: New Challenges and Perspectives for the New Millennium
Publisher	IEEE
Pages	411-416
ISBN (Print)	0-7695-0619-4, 0-7803-6541-0
DOIs	https://doi.org/10.1109/IJCNN.2000.859430
Publication status	Published - Jul 2000
Externally published	Yes
Event	International Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy Duration: 24 Jul 2000 → 27 Jul 2000

Publication series

Name
Volume	6

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'2000)
City	Como, Italy
Period	24/07/00 → 27/07/00

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@inproceedings{38ba27400d5948298f8bf446803d5680,

title = "Neural network realization of support vector methods for pattern classification",

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.",

author = "Ying Tan and Youshen Xia and Jun Wang",

year = "2000",

month = jul,

doi = "10.1109/IJCNN.2000.859430",

language = "English",

isbn = "0-7695-0619-4",

publisher = "IEEE",

pages = "411--416",

booktitle = "Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000)",

address = "United States",

note = "International Joint Conference on Neural Networks (IJCNN'2000) ; Conference date: 24-07-2000 Through 27-07-2000",

}

Tan, Y, Xia, Y & Wang, J 2000, Neural network realization of support vector methods for pattern classification. in Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000) : Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, pp. 411-416, International Joint Conference on Neural Networks (IJCNN'2000), Como, Italy, 24/07/00. https://doi.org/10.1109/IJCNN.2000.859430

Neural network realization of support vector methods for pattern classification. / Tan, Ying; Xia, Youshen; Wang, Jun.
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000) : Neural Computing: New Challenges and Perspectives for the New Millennium. IEEE, 2000. p. 411-416.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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T1 - Neural network realization of support vector methods for pattern classification

AU - Tan, Ying

AU - Xia, Youshen

AU - Wang, Jun

PY - 2000/7

Y1 - 2000/7

N2 - 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.

AB - 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.

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DO - 10.1109/IJCNN.2000.859430

M3 - RGC 32 - Refereed conference paper (with host publication)

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SN - 0-7803-6541-0

SP - 411

EP - 416

BT - Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000)

PB - IEEE

T2 - International Joint Conference on Neural Networks (IJCNN'2000)

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ER -

Neural network realization of support vector methods for pattern classification

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