On the construction of Support Wavelet Network

Junbin Gao, Daming Shi, Fei Chen

Research output: Book chapter/Published conference paperConference paper

5 Citations (Scopus)
4 Downloads (Pure)

Abstract

Wavelet networks have emerged as a powerful tool for nonparametric estimation. It is a method implementing inverse discrete wavelet transform with coefficient optimization techniques from machine learning field. However, conventional ways to construct wavelet networks are based on empirical risk minimization (ERM) principle, which has been proven not as robust as structural risk minimization (SRM) principle. Thus, to explore the optimal architecture of wavelet networks, we constructed wavelet networks based on SRM principle. This paper describes the kernel-based way to optimize the architecture of wavelet networks. Based on the frame theory, wavelet kernel functions are found. After that, the wavelet network is constructed with support vectors generated by the wavelet kernel functions.
Original languageEnglish
Title of host publication2004 IEEE International Conference on Systems, Man & Cybernetics
Place of PublicationUSA
PublisherIEEE
Pages3204-3207
Number of pages4
Volume4
DOIs
Publication statusPublished - 2004
EventIEEE Conference on Systems, Man and Cybernetics - The Hague, The Netherlands, Netherlands
Duration: 10 Oct 200413 Oct 2004

Conference

ConferenceIEEE Conference on Systems, Man and Cybernetics
CountryNetherlands
Period10/10/0413/10/04

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Discrete wavelet transforms
Learning systems

Cite this

Gao, J., Shi, D., & Chen, F. (2004). On the construction of Support Wavelet Network. In 2004 IEEE International Conference on Systems, Man & Cybernetics (Vol. 4, pp. 3204-3207). USA: IEEE. https://doi.org/10.1109/ICSMC.2004.1400833
Gao, Junbin ; Shi, Daming ; Chen, Fei. / On the construction of Support Wavelet Network. 2004 IEEE International Conference on Systems, Man & Cybernetics. Vol. 4 USA : IEEE, 2004. pp. 3204-3207
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title = "On the construction of Support Wavelet Network",
abstract = "Wavelet networks have emerged as a powerful tool for nonparametric estimation. It is a method implementing inverse discrete wavelet transform with coefficient optimization techniques from machine learning field. However, conventional ways to construct wavelet networks are based on empirical risk minimization (ERM) principle, which has been proven not as robust as structural risk minimization (SRM) principle. Thus, to explore the optimal architecture of wavelet networks, we constructed wavelet networks based on SRM principle. This paper describes the kernel-based way to optimize the architecture of wavelet networks. Based on the frame theory, wavelet kernel functions are found. After that, the wavelet network is constructed with support vectors generated by the wavelet kernel functions.",
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author = "Junbin Gao and Daming Shi and Fei Chen",
note = "Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE, 2004. Event dates (773o) = 10-13 October 2004; Parent title (773t) = IEEE Conference on Systems, Man and Cybernetics. ISSNs: 1062-922X;",
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Gao, J, Shi, D & Chen, F 2004, On the construction of Support Wavelet Network. in 2004 IEEE International Conference on Systems, Man & Cybernetics. vol. 4, IEEE, USA, pp. 3204-3207, IEEE Conference on Systems, Man and Cybernetics, Netherlands, 10/10/04. https://doi.org/10.1109/ICSMC.2004.1400833

On the construction of Support Wavelet Network. / Gao, Junbin; Shi, Daming; Chen, Fei.

2004 IEEE International Conference on Systems, Man & Cybernetics. Vol. 4 USA : IEEE, 2004. p. 3204-3207.

Research output: Book chapter/Published conference paperConference paper

TY - GEN

T1 - On the construction of Support Wavelet Network

AU - Gao, Junbin

AU - Shi, Daming

AU - Chen, Fei

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE, 2004. Event dates (773o) = 10-13 October 2004; Parent title (773t) = IEEE Conference on Systems, Man and Cybernetics. ISSNs: 1062-922X;

PY - 2004

Y1 - 2004

N2 - Wavelet networks have emerged as a powerful tool for nonparametric estimation. It is a method implementing inverse discrete wavelet transform with coefficient optimization techniques from machine learning field. However, conventional ways to construct wavelet networks are based on empirical risk minimization (ERM) principle, which has been proven not as robust as structural risk minimization (SRM) principle. Thus, to explore the optimal architecture of wavelet networks, we constructed wavelet networks based on SRM principle. This paper describes the kernel-based way to optimize the architecture of wavelet networks. Based on the frame theory, wavelet kernel functions are found. After that, the wavelet network is constructed with support vectors generated by the wavelet kernel functions.

AB - Wavelet networks have emerged as a powerful tool for nonparametric estimation. It is a method implementing inverse discrete wavelet transform with coefficient optimization techniques from machine learning field. However, conventional ways to construct wavelet networks are based on empirical risk minimization (ERM) principle, which has been proven not as robust as structural risk minimization (SRM) principle. Thus, to explore the optimal architecture of wavelet networks, we constructed wavelet networks based on SRM principle. This paper describes the kernel-based way to optimize the architecture of wavelet networks. Based on the frame theory, wavelet kernel functions are found. After that, the wavelet network is constructed with support vectors generated by the wavelet kernel functions.

KW - Open access version available

KW - Neural Networks

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BT - 2004 IEEE International Conference on Systems, Man & Cybernetics

PB - IEEE

CY - USA

ER -

Gao J, Shi D, Chen F. On the construction of Support Wavelet Network. In 2004 IEEE International Conference on Systems, Man & Cybernetics. Vol. 4. USA: IEEE. 2004. p. 3204-3207 https://doi.org/10.1109/ICSMC.2004.1400833