메뉴 건너뛰기




Volumn , Issue , 2007, Pages 1714-1719

Upper bound on pattern storage in feedforward networks

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER NETWORKS; CONJUGATE GRADIENT METHOD; MULTILAYER NEURAL NETWORKS; SUPPORT VECTOR MACHINES; VECTORS;

EID: 51749113577     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2007.4371216     Document Type: Conference Paper
Times cited : (5)

References (27)
  • 1
    • 49149123371 scopus 로고    scopus 로고
    • Hybrid hot strip rolling force prediction using a bayesian trained artificial neural network and analytical models
    • Y. S. A. Moussaoui, H. A. Abbassi, Hybrid hot strip rolling force prediction using a bayesian trained artificial neural network and analytical models, American Journal of Applied Sciences 3 (6) (2006) 1885-1889.
    • (2006) American Journal of Applied Sciences , vol.3 , Issue.6 , pp. 1885-1889
    • Moussaoui, Y.S.A.1    Abbassi, H.A.2
  • 3
    • 0001632845 scopus 로고
    • On the capabilities of multilayer perceptrons
    • E. B. Baum, On the capabilities of multilayer perceptrons, Journal of Complexity 4 (1988) 193-215.
    • (1988) Journal of Complexity , vol.4 , pp. 193-215
    • Baum, E.B.1
  • 4
    • 43949154615 scopus 로고
    • Bounds on the number of units for computing arbitrary dichotomies by multilayer perceptrons
    • M. Cosnard, P. Koiran, H. Paugam-Moisy, Bounds on the number of units for computing arbitrary dichotomies by multilayer perceptrons, Journal of Complexity 10 (1994) 57-63.
    • (1994) Journal of Complexity , vol.10 , pp. 57-63
    • Cosnard, M.1    Koiran, P.2    Paugam-Moisy, H.3
  • 8
    • 0026190194 scopus 로고
    • A Simple Method to Derive Bounds on the Size and to Train Multilayer Neural Networks
    • Jul
    • M. A. Sartori and P. J. Antsaklis, "A Simple Method to Derive Bounds on the Size and to Train Multilayer Neural Networks," IEEE Transactions on Neural Networks, vol. 2, no. 4, pp. 467-471, Jul. 1991.
    • (1991) IEEE Transactions on Neural Networks , vol.2 , Issue.4 , pp. 467-471
    • Sartori, M.A.1    Antsaklis, P.J.2
  • 9
    • 0031673055 scopus 로고    scopus 로고
    • Upper Bounds on the Number of Hidden Neurons in Feedforward Networks with Arbitrary Bounded Nonlinear Activation Function
    • Jan
    • G-B. Huang and H. A. Babri, "Upper Bounds on the Number of Hidden Neurons in Feedforward Networks with Arbitrary Bounded Nonlinear Activation Function," IEEE Transactions in Neural Networks, vol. 9, no. 1, pp. 224-229, Jan. 1998.
    • (1998) IEEE Transactions in Neural Networks , vol.9 , Issue.1 , pp. 224-229
    • Huang, G.-B.1    Babri, H.A.2
  • 10
    • 0032594959 scopus 로고    scopus 로고
    • An overview of Statistical Learning Theory
    • Sep
    • V. N. Vapnik, "An overview of Statistical Learning Theory," IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 988-999, Sep. 1999.
    • (1999) IEEE Transactions on Neural Networks , vol.10 , Issue.5 , pp. 988-999
    • Vapnik, V.N.1
  • 12
    • 51749122237 scopus 로고    scopus 로고
    • H. Jeffreys, B. S. Jeffreys, Methods of Mathematical Physics, chap. Lagrange's Interpolation Formula, 3rd ed., Cambridge University Press, Cambridge, England, 1988, p. §9.011 pp. 260.
    • H. Jeffreys, B. S. Jeffreys, Methods of Mathematical Physics, chap. Lagrange's Interpolation Formula, 3rd ed., Cambridge University Press, Cambridge, England, 1988, p. §9.011 pp. 260.
  • 13
    • 0031701574 scopus 로고    scopus 로고
    • Capacity of two-layer feedforward neural networks with binary weights
    • C. Ji, D. Psaltis, Capacity of two-layer feedforward neural networks with binary weights, IEEE Transaction on Information Theory 44 (1) (1998) 256-268.
    • (1998) IEEE Transaction on Information Theory , vol.44 , Issue.1 , pp. 256-268
    • Ji, C.1    Psaltis, D.2
  • 17
    • 0033362397 scopus 로고    scopus 로고
    • Performance and efficiency: Recent advances in supervised learning
    • S. Ma, C. Ji, Performance and efficiency: Recent advances in supervised learning, Proceedings of the IEEE 87 (9) (1999) 1519-1535.
    • (1999) Proceedings of the IEEE , vol.87 , Issue.9 , pp. 1519-1535
    • Ma, S.1    Ji, C.2
  • 18
    • 0031171661 scopus 로고    scopus 로고
    • On the storage capacity of nonlinear neural networks
    • C. Mazza, On the storage capacity of nonlinear neural networks, Neural Networks 10 (4) (1997) 593-597.
    • (1997) Neural Networks , vol.10 , Issue.4 , pp. 593-597
    • Mazza, C.1
  • 19
    • 0026868102 scopus 로고
    • Functional-link net computing: Theory, system architecture, and functionalities
    • Y.-H. Pao, Y. Takefuji, Functional-link net computing: Theory, system architecture, and functionalities, IEEE Computer 25 (5) (1992) 76-79.
    • (1992) IEEE Computer , vol.25 , Issue.5 , pp. 76-79
    • Pao, Y.-H.1    Takefuji, Y.2
  • 20
    • 0000460775 scopus 로고
    • Radial basis function approximations to polynomials
    • M. J. D. Powell, Radial basis function approximations to polynomials, Numerical Analysis 1987 Proceedings (1988) 223-241.
    • (1988) Numerical Analysis 1987 Proceedings , pp. 223-241
    • Powell, M.J.D.1
  • 21
    • 0033316275 scopus 로고    scopus 로고
    • New approach to the storage capacity of neural networks using the minimum distance between input patterns
    • H. Suyari, I. Matsuba, New approach to the storage capacity of neural networks using the minimum distance between input patterns, in: International Joint Conference on Neural Networks, vol. 1, 1999.
    • (1999) International Joint Conference on Neural Networks , vol.1
    • Suyari, H.1    Matsuba, I.2
  • 22
    • 0037507242 scopus 로고    scopus 로고
    • Pruning Error Minimization in Least Squares Support Vector Machines
    • May
    • B. J. de Kruif and T. J. A. de Vries, "Pruning Error Minimization in Least Squares Support Vector Machines," IEEE Transactions on Neural Networks, vol. 14, no. 3, pp. 696-702, May 2003.
    • (2003) IEEE Transactions on Neural Networks , vol.14 , Issue.3 , pp. 696-702
    • de Kruif, B.J.1    de Vries, T.J.A.2
  • 24
    • 10044224122 scopus 로고
    • Non-Gaussian Feature Analyses Using a Neural Network,
    • W. Gong, H. C. Yau and M. T. Manry, "Non-Gaussian Feature Analyses Using a Neural Network,", Progress in Neural Networks, vol. 2, pp. 253-269, 1994.
    • (1994) Progress in Neural Networks , vol.2 , pp. 253-269
    • Gong, W.1    Yau, H.C.2    Manry, M.T.3
  • 27
    • 51749093780 scopus 로고    scopus 로고
    • C. Chang and C. Lin. LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.
    • C. Chang and C. Lin. LIBSVM: A library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, 2001.


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.