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Volumn 2, Issue , 2000, Pages 672-674

RBF two-stage learning networks exploiting supervised data in the selection of hidden unit parameters: An application to SAR data classification

Author keywords

[No Author keywords available]

Indexed keywords

RADIAL BASIS FUNCTIONS (RBF);

EID: 0034542091     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (7)

References (10)
  • 2
    • 0031270611 scopus 로고    scopus 로고
    • Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques
    • N. Karayiannis, "Growing radial basis neural networks: Merging supervised and unsupervised learning with network growth techniques," IEEE Trans. on Neural Neworks, vol. 8, no. 6, pp. 1492-1506, 1997.
    • (1997) IEEE Trans. on Neural Neworks , vol.8 , Issue.6 , pp. 1492-1506
    • Karayiannis, N.1
  • 3
    • 84873998733 scopus 로고    scopus 로고
    • Reformulated radial basis neural networks trained by gradient descent
    • under review
    • N. Karayiannis, "Reformulated radial basis neural networks trained by gradient descent," IEEE Trans. on Neural Networks, under review, 1998.
    • (1998) IEEE Trans. on Neural Networks
    • Karayiannis, N.1
  • 4
    • 0028748949 scopus 로고
    • Growing cell structures - A self-organizing network for unsupervised and supervised learning
    • B. Fritzke, "Growing cell structures - A self-organizing network for unsupervised and supervised learning," Neural Networks, vol. 7, no. 9, pp. 1441-1460, 1994.
    • (1994) Neural Networks , vol.7 , Issue.9 , pp. 1441-1460
    • Fritzke, B.1
  • 5
    • 0033099197 scopus 로고    scopus 로고
    • A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images
    • L. Bruzzone and D. F. Prieto, "A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images," IEEE Trans. Geosci. and Remote Sensing, vol. 37, no.2, pp. 1179-1184, 1999.
    • (1999) IEEE Trans. Geosci. and Remote Sensing , vol.37 , Issue.2 , pp. 1179-1184
    • Bruzzone, L.1    Prieto, D.F.2
  • 10
    • 0031069945 scopus 로고    scopus 로고
    • The LBG-U method for vector quantization - An improvement over LBG inspired from neural networks
    • B. Fritzke, "The LBG-U method for vector quantization - An improvement over LBG inspired from neural networks," Neural Processing Letters, vol. 5, no. 1, 1997.
    • (1997) Neural Processing Letters , vol.5 , Issue.1
    • Fritzke, B.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.