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Volumn 15, Issue 1, 2003, Pages 183-212

A quantified sensitivity measure for multilayer perceptron to input perturbation

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; EVALUATION; SENSITIVITY AND SPECIFICITY;

EID: 0037264956     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976603321043757     Document Type: Article
Times cited : (57)

References (14)
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    • Choi, J.Y.1    Choi, C.H.2
  • 4
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    • Incremental learning using sensitivity analysis
    • Washington, DC
    • Engelbrecht, A. P., & Cloete, I. (1999). Incremental learning using sensitivity analysis. In Proceedings of IEEE IJCNN'99 (Vol. 2, pp. 1350-1355). Washington, DC.
    • (1999) Proceedings of IEEE IJCNN'99 , vol.2 , pp. 1350-1355
    • Engelbrecht, A.P.1    Cloete, I.2
  • 5
    • 0033308459 scopus 로고    scopus 로고
    • Variance analysis of sensitivity information for pruning multilayer feedforward neural networks
    • Washington, DC
    • Engelbrecht, A. P., Fletcher, L., & Cloete, I. (1999). Variance analysis of sensitivity information for pruning multilayer feedforward neural networks. In Proceedings of IEEE IJCNN'99 (Vol. 3, pp. 1829-1833). Washington, DC.
    • (1999) Proceedings of IEEE IJCNN'99 , vol.3 , pp. 1829-1833
    • Engelbrecht, A.P.1    Fletcher, L.2    Cloete, I.3
  • 6
    • 84943261597 scopus 로고
    • Sensitivity analysis for input vector in multilayer feedforward neural networks
    • San Francisco
    • Fu, L., & Chen, T. (1993). Sensitivity analysis for input vector in multilayer feedforward neural networks. In Proceedings of IEEE International Conference on Neural Networks (Vol. 1, pp. 215-218). San Francisco.
    • (1993) Proceedings of IEEE International Conference on Neural Networks , vol.1 , pp. 215-218
    • Fu, L.1    Chen, T.2
  • 7
    • 85084737696 scopus 로고
    • Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions
    • Baltimore, MD
    • Hashem, S. (1992). Sensitivity analysis for feedforward artificial neural networks with differentiable activation functions. In Proceedings of IJCNN'92 (Vol. 1, pp. 419-424). Baltimore, MD.
    • (1992) Proceedings of IJCNN'92 , vol.1 , pp. 419-424
    • Hashem, S.1
  • 8
    • 0028420749 scopus 로고
    • Input noise immunity of multilayer perceptrons
    • Lee, Y., & Oh, S. H. (1994). Input noise immunity of multilayer perceptrons. ETRI Journal, 16(1), 35-43.
    • (1994) ETRI Journal , vol.16 , Issue.1 , pp. 35-43
    • Lee, Y.1    Oh, S.H.2
  • 9
    • 0029267941 scopus 로고
    • The selection of weight accuracies for madalines
    • Piché, S. W. (1995). The selection of weight accuracies for madalines. IEEE Transactions on Neural Networks, 6(2), 432-445.
    • (1995) IEEE Transactions on Neural Networks , vol.6 , Issue.2 , pp. 432-445
    • Piché, S.W.1
  • 11
    • 0036129214 scopus 로고    scopus 로고
    • Using function approximation to analyze the sensitivity of MLP with antisymmetric squashing activation function
    • Yeung, D. S., & Sun, X. (2002). Using function approximation to analyze the sensitivity of MLP with antisymmetric squashing activation function. IEEE Transactions on Neural Networks, 13(1), 34-44.
    • (2002) IEEE Transactions on Neural Networks , vol.13 , Issue.1 , pp. 34-44
    • Yeung, D.S.1    Sun, X.2
  • 13
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    • Sensitivity analysis of multilayer perceptron to input and weight perturbations
    • Zeng, X., & Yeung, D. S. (2001). Sensitivity analysis of multilayer perceptron to input and weight perturbations. IEEE Transactions on Neural Networks, 12(6), 1358-1366.
    • (2001) IEEE Transactions on Neural Networks , vol.12 , Issue.6 , pp. 1358-1366
    • Zeng, X.1    Yeung, D.S.2
  • 14
    • 0031553665 scopus 로고    scopus 로고
    • Perturbation method for deleting redundant inputs of perceptron networks
    • Zurada, J. M., Malinowski, A., & Usui, S. (1997). Perturbation method for deleting redundant inputs of perceptron networks. Neurocomputing, 14, 177-193.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.