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Volumn 11, Issue 4, 1999, Pages 977-993

Pruning using parameter and neuronal metrics

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; AMERICAN INDIAN; ARTICLE; ARTIFICIAL NEURAL NETWORK; DIABETES MELLITUS; NERVE CELL; PATHOPHYSIOLOGY; PHYSIOLOGY; PROBABILITY; REPRODUCIBILITY;

EID: 0033561861     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976699300016548     Document Type: Article
Times cited : (20)

References (27)
  • 1
    • 0016355478 scopus 로고
    • A new look at the statistical model identification
    • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723.
    • (1974) IEEE Transactions on Automatic Control , vol.19 , Issue.6 , pp. 716-723
    • Akaike, H.1
  • 2
    • 0000396062 scopus 로고    scopus 로고
    • Natural gradient works efficiently in learning
    • Amari, S.-I. (1998). Natural gradient works efficiently in learning. Neural Computation, 10(2), 251-276.
    • (1998) Neural Computation , vol.10 , Issue.2 , pp. 251-276
    • Amari, S.-I.1
  • 4
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L. (1996). Bagging predictors, Machine Learning 24(2), 123-140.
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 6
    • 0040481047 scopus 로고
    • The omission or addition of an independent variate in multiple linear regression
    • Cochran, W. G. (1938). The omission or addition of an independent variate in multiple linear regression. Supplement to the Journal of the Royal Statistical Society 5(2), 171-176.
    • (1938) Supplement to the Journal of the Royal Statistical Society , vol.5 , Issue.2 , pp. 171-176
    • Cochran, W.G.1
  • 9
    • 0000155950 scopus 로고
    • The cascade-correlation learning architecture
    • D. S. Touretzky (Ed.), San Mateo, CA: Morgan Kaufmann
    • Fahlman, S. E., & Lebiere, C. (1990). The cascade-correlation learning architecture. In D. S. Touretzky (Ed.), Advances in neural information processing systems 2 (pp. 524-532). San Mateo, CA: Morgan Kaufmann.
    • (1990) Advances in Neural Information Processing Systems , vol.2 , pp. 524-532
    • Fahlman, S.E.1    Lebiere, C.2
  • 11
    • 0001234705 scopus 로고
    • Second order derivatives for network pruning: Optimal brain surgeon
    • S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), San Mateo, CA: Morgan Kaufmann
    • Hassibi, B., & Stork, D. G. (1993). Second order derivatives for network pruning: Optimal Brain Surgeon. In S. J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in neural information processing systems, 5 (pp. 164-171). San Mateo, CA: Morgan Kaufmann.
    • (1993) Advances in Neural Information Processing Systems , vol.5 , pp. 164-171
    • Hassibi, B.1    Stork, D.G.2
  • 12
    • 0000833730 scopus 로고
    • Optimal brain surgeon: Extensions and performance comparisons
    • J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), San Mateo, CA: Morgan Kaufmann
    • Hassibi, B., Stork, D. G., Wolff, G., & Watanabe, T. (1994). Optimal Brain Surgeon: Extensions and performance comparisons. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in neural information processing systems, 6 (pp. 263-270). San Mateo, CA: Morgan Kaufmann.
    • (1994) Advances in Neural Information Processing Systems , vol.6 , pp. 263-270
    • Hassibi, B.1    Stork, D.G.2    Wolff, G.3    Watanabe, T.4
  • 13
    • 0025964567 scopus 로고
    • Back-propagation algorithm which varies the number of hidden units
    • Hirose, Y., Yamashita, K., & Hijiya, S. (1991). Back-propagation algorithm which varies the number of hidden units. Neural Networks 4(1), 61-66.
    • (1991) Neural Networks , vol.4 , Issue.1 , pp. 61-66
    • Hirose, Y.1    Yamashita, K.2    Hijiya, S.3
  • 14
    • 0030130724 scopus 로고    scopus 로고
    • Structural learning with forgetting
    • Ishikawa, M. (1996). Structural learning with forgetting. Neural Networks, 9(3), 509-521.
    • (1996) Neural Networks , vol.9 , Issue.3 , pp. 509-521
    • Ishikawa, M.1
  • 17
    • 0001441372 scopus 로고
    • Probable networks and plausible predictions - A review of practical Bayesian methods for supervised neural networks
    • MacKay, D. J. C. (1995). Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems, 6(3), 469-505.
    • (1995) Network: Computation in Neural Systems , vol.6 , Issue.3 , pp. 469-505
    • MacKay, D.J.C.1
  • 18
    • 0029754431 scopus 로고    scopus 로고
    • The dependence identification neural network construction algorithm
    • Moody, J. O., & Antsaklis, P. J. (1996). The dependence identification neural network construction algorithm. IEEE Transactions on Neural Networks, 7(1), 3-15.
    • (1996) IEEE Transactions on Neural Networks , vol.7 , Issue.1 , pp. 3-15
    • Moody, J.O.1    Antsaklis, P.J.2
  • 19
    • 0028544395 scopus 로고
    • Network information criterion - Determining the number of hidden units for an artificial neural network model
    • Murata, N., Yoshizawa, S., & Amari, S.-I. (1994). Network information criterion - Determining the number of hidden units for an artificial neural network model. IEEE Transactions on Neural Networks, 5(6), 865-872.
    • (1994) IEEE Transactions on Neural Networks , vol.5 , Issue.6 , pp. 865-872
    • Murata, N.1    Yoshizawa, S.2    Amari, S.-I.3
  • 20
    • 85156214178 scopus 로고    scopus 로고
    • Pruning with generalization based weight saliencies: γOBD, γOBS
    • D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Cambridge, MA: MIT Press
    • Pedersen, M. W., Hansen, L. K., & Larsen, J. (1996). Pruning with generalization based weight saliencies: γOBD, γOBS. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems, 8 (pp. 521-527). Cambridge, MA: MIT Press.
    • (1996) Advances in Neural Information Processing Systems , vol.8 , pp. 521-527
    • Pedersen, M.W.1    Hansen, L.K.2    Larsen, J.3
  • 22
  • 23
    • 0018015137 scopus 로고
    • Modeling by shortest data description
    • Rissanen, J. (1978). Modeling by shortest data description. Automatica, 14, 465-471.
    • (1978) Automatica , vol.14 , pp. 465-471
    • Rissanen, J.1
  • 24
    • 84899002752 scopus 로고    scopus 로고
    • Fast network pruning and feature extraction using the unit-OBS algorithm
    • M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Cambridge, MA: MIT Press
    • Stahlberger, A., & Riedmiller, M. (1997). Fast network pruning and feature extraction using the unit-OBS algorithm. In M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems, 9 (pp. 655-661). Cambridge, MA: MIT Press.
    • (1997) Advances in Neural Information Processing Systems , vol.9 , pp. 655-661
    • Stahlberger, A.1    Riedmiller, M.2
  • 26
    • 84956603004 scopus 로고    scopus 로고
    • Input selection with partial retraining
    • W. Gerstner, A. Germond, M. Hasler, & J.-D. Nicoud (Eds.), Berlin: Springer
    • van de Laar, P., Gielen, S., & Heskes, T. (1997). Input selection with partial retraining. In W. Gerstner, A. Germond, M. Hasler, & J.-D. Nicoud (Eds.), Artificial neural networks - ICANN'97 (pp. 469-474). Berlin: Springer, pp. 469-474.
    • (1997) Artificial Neural Networks - ICANN'97 , pp. 469-474
    • Van De Laar, P.1    Gielen, S.2    Heskes, T.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.