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Volumn 5, Issue 1, 1996, Pages 269-280

Linear unlearning for cross-validation

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[No Author keywords available]

Indexed keywords


EID: 21344466221     PISSN: 10197168     EISSN: None     Source Type: Journal    
DOI: 10.1007/bf02124747     Document Type: Article
Times cited : (43)

References (15)
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    • Generalization performance of regularized neural network models
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    • Larsen, J.1    Hansen, L.K.2
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    • Empirical generalization assessment of neural network models
    • eds. F. Girosi, J. Makhoul, E. Manolakos and E. Wilson, IEEE, Piscataway, NJ
    • J. Larsen and L.K. Hansen, Empirical generalization assessment of neural network models, in: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing V, eds. F. Girosi, J. Makhoul, E. Manolakos and E. Wilson, IEEE, Piscataway, NJ, 1995, pp. 30-39.
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    • Note on generalization, regularization, and architecture selection in nonlinear learning systems
    • eds. B.H. Juang, S.Y. Kung and C.A. Kamm, IEEE, Piscataway, NJ
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    • (1991) Proceedings of the 1st IEEE Workshop on Neural Networks for Signal Processing , pp. 1-10
    • Moody, J.1
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    • Prediction risk and architecture selection for neural networks
    • eds. V. Cherkassky, J.H. Friedman and H. Wechsler, Series F, Springer, Berlin
    • J. Moody, Prediction risk and architecture selection for neural networks, in: From Statistics to Neural Networks: Theory and Pattern Recognition Applications, eds. V. Cherkassky, J.H. Friedman and H. Wechsler, Series F, vol. 136, Springer, Berlin, 1994.
    • (1994) From Statistics to Neural Networks: Theory and Pattern Recognition Applications , vol.136
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  • 10
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    • Network information criterion - Determining the number of hidden units for an artificial neural network model
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    • Bibliography on estimation of misclassification
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.