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Volumn 11, Issue 4, 1999, Pages 965-976

An adaptive Bayesian pruning for neural networks in a non-stationary environment

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CHRONIC BRAIN DISEASE; FILTRATION; LEARNING; NORMAL DISTRIBUTION; TIME;

EID: 0033561887     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976699300016539     Document Type: Article
Times cited : (12)

References (18)
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  • 3
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    • Iiguni, Y.1    Sakai, H.2    Tokumaru, H.3
  • 5
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    • An adaptive least squares algorithm for the efficient training of artificial neural networks
    • Kollias, S., & Anastassiou, D. (1989). An adaptive least squares algorithm for the efficient training of artificial neural networks. IEEE Transactions on Circuits and Systems, 36(8), 1092-1101.
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  • 6
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    • Unpublished doctoral disseration, Department of Mathematical Modeling, Technical University of Denmark
    • Larsen J. (1996). Design of neural network filters. Unpublished doctoral disseration, Department of Mathematical Modeling, Technical University of Denmark.
    • (1996) Design of Neural Network Filters
    • Larsen, J.1
  • 8
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    • On-line training and pruning for RLS algorithms
    • Leung, C. S., Wong, K. W., Sum, P. F., & Chan, L. W. (1996). On-line training and pruning for RLS algorithms. Electronics Letter, 32, 2152-2153.
    • (1996) Electronics Letter , vol.32 , pp. 2152-2153
    • Leung, C.S.1    Wong, K.W.2    Sum, P.F.3    Chan, L.W.4
  • 9
    • 0002704818 scopus 로고
    • A practical Bayesian framework for backprop networks
    • MacKay, D. J. C. (1992). A practical Bayesian framework for backprop networks. Neural Computation, 4(3), 448-472.
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    • in press
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.