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Volumn 8, Issue 7, 1996, Pages 1521-1539

Learning and Generalization in Cascade Network Architectures

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; HUMAN; LEARNING; PHYSIOLOGY;

EID: 0030267612     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/neco.1996.8.7.1521     Document Type: Article
Times cited : (13)

References (32)
  • 1
    • 0005760958 scopus 로고
    • Memory-based approaches to approximating continuous function
    • Nonlinear Modeling and Forecasting, M. Casdagli, and S. Eubank, eds., Addison-Wesley, New York
    • Atkeson, C. 1992. Memory-based approaches to approximating continuous function. In Nonlinear Modeling and Forecasting, M. Casdagli, and S. Eubank, eds., Vol. XII of SFI Studies in the Sciences of Complexity, pp. 503-521. Addison-Wesley, New York.
    • (1992) SFI Studies in the Sciences of Complexity , vol.12 , pp. 503-521
    • Atkeson, C.1
  • 2
    • 85033152836 scopus 로고
    • Growing layers of perceptrons: Introducing the extentron algorithm
    • Baffes, P., and Zelle, J. 1992. Growing layers of perceptrons: Introducing the extentron algorithm. Proc. Int. Joint Conf. Neural Networks, Vol. II, pp. 392-397.
    • (1992) Proc. Int. Joint Conf. Neural Networks , vol.2 , pp. 392-397
    • Baffes, P.1    Zelle, J.2
  • 3
    • 0001160588 scopus 로고
    • What size net gives valid generalization?
    • Baum, E., and Haussler, D. 1989. What size net gives valid generalization? Neural Comp. 1, 151-160.
    • (1989) Neural Comp. , vol.1 , pp. 151-160
    • Baum, E.1    Haussler, D.2
  • 4
    • 45549110576 scopus 로고
    • Regression by local fitting: Methods, properties, and computational algorithms
    • Cleveland, W., Devlin, S., and Grosse, E. 1988. Regression by local fitting: Methods, properties, and computational algorithms. Nonlinear Model. Forecast. J. Economet. 37, 87-114.
    • (1988) Nonlinear Model. Forecast. J. Economet. , vol.37 , pp. 87-114
    • Cleveland, W.1    Devlin, S.2    Grosse, E.3
  • 5
    • 0003093755 scopus 로고
    • Predicting the Mackey-Glass timeseries with cascade-correlation learning
    • D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, eds., Morgan Kaufmann, San Mateo, CA
    • Crowder, R. S. 1990. Predicting the Mackey-Glass timeseries with cascade-correlation learning. In Connectionist Models: Proceedings of the 1990 Summer School, D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, eds., pp. 524-532. Morgan Kaufmann, San Mateo, CA.
    • (1990) Connectionist Models: Proceedings of the 1990 Summer School , pp. 524-532
    • Crowder, R.S.1
  • 6
    • 2342486398 scopus 로고
    • Personal communication
    • Fahlman, S. E. 1992. Personal communication.
    • (1992)
    • Fahlman, S.E.1
  • 7
    • 0000155950 scopus 로고
    • The cascade-correlation learning architecture
    • D. S. Touretzky, ed., Morgan Kaufmann, San Mateo, CA
    • Fahlman, S. E., and Lebiere, C. 1990. The cascade-correlation learning architecture. In Advances in Neural Information Processing Systems 2, D. S. Touretzky, ed., pp. 524-532. Morgan Kaufmann, San Mateo, CA.
    • (1990) Advances in Neural Information Processing Systems 2 , vol.2 , pp. 524-532
    • Fahlman, S.E.1    Lebiere, C.2
  • 8
    • 34249982739 scopus 로고
    • Predicting chaotic time series
    • Farmer, J. and Sidorowich, J. 1987. Predicting chaotic time series. Phys. Rev. Lett. 59, 845-848.
    • (1987) Phys. Rev. Lett. , vol.59 , pp. 845-848
    • Farmer, J.1    Sidorowich, J.2
  • 9
    • 0000783575 scopus 로고
    • The upstart algorithm: A method for constructing and training feedforward neural networks
    • Frean, M. 1990. The upstart algorithm: A method for constructing and training feedforward neural networks. Neural Comp. 2, 198-209.
    • (1990) Neural Comp. , vol.2 , pp. 198-209
    • Frean, M.1
  • 10
    • 84945709355 scopus 로고
    • An algorithm for finding best matches in logarithmic expected time
    • Friedman, J. H., Bentley, J. L., and Finkel, R. A. 1977. An algorithm for finding best matches in logarithmic expected time. ACM Transact. Math. Software 3, 209-226.
    • (1977) ACM Transact. Math. Software , vol.3 , pp. 209-226
    • Friedman, J.H.1    Bentley, J.L.2    Finkel, R.A.3
  • 11
    • 0000067815 scopus 로고
    • Predicting the future: Advantages of semilocal units
    • Hartmann, E., and Keeler, J. D. 1991. Predicting the future: Advantages of semilocal units. Neural Comp. 3, 566-578.
    • (1991) Neural Comp. , vol.3 , pp. 566-578
    • Hartmann, E.1    Keeler, J.D.2
  • 13
    • 0003527079 scopus 로고
    • Springer Series in Information Sciences 8. Springer-Verlag, Berlin
    • Kohonen, T. 1982. Self Organization and Associative Memory. Springer Series in Information Sciences 8. Springer-Verlag, Berlin.
    • (1982) Self Organization and Associative Memory
    • Kohonen, T.1
  • 14
    • 0024124595 scopus 로고
    • Statistical pattern recognition with neural networks: Bench-marking studies
    • IEEE Neural Network Council
    • Kohonen, T. 1988. Statistical pattern recognition with neural networks: Bench-marking studies. Proc. ICNN. IEEE Neural Network Council.
    • (1988) Proc. ICNN.
    • Kohonen, T.1
  • 15
    • 0025489075 scopus 로고
    • The self-organizing map
    • Kohonen, T. 1990. The self-organizing map. Proceedings IEEE, 78, 1464-1480.
    • (1990) Proceedings IEEE , vol.78 , pp. 1464-1480
    • Kohonen, T.1
  • 20
    • 0017714604 scopus 로고
    • Oscillations and chaos in physiological control systems
    • Mackey, M., and Glass, L. 1977. Oscillations and chaos in physiological control systems. Science 197, 287-289.
    • (1977) Science , vol.197 , pp. 287-289
    • Mackey, M.1    Glass, L.2
  • 21
    • 36149031331 scopus 로고
    • Learning in feedforward layered networks: The tiling algorithm
    • Mezard, M., and Nadal, J.-P. 1989. Learning in feedforward layered networks: The tiling algorithm. J. Phys. A 22, 2191-2204.
    • (1989) J. Phys. A , vol.22 , pp. 2191-2204
    • Mezard, M.1    Nadal, J.-P.2
  • 23
    • 0028255785 scopus 로고
    • Toward generating neural network structures for function approximation
    • Nabhan, T., and Zomaya, A. 1994. Toward generating neural network structures for function approximation. Neural Networks, 7, 89-99.
    • (1994) Neural Networks , vol.7 , pp. 89-99
    • Nabhan, T.1    Zomaya, A.2
  • 24
    • 0010069683 scopus 로고
    • Evaluation of adaptive mixtures of competing experts
    • D. S. Touretzky, ed., Morgan Kaufmann, San Mateo, CA
    • Nowlan, S. J., and Hinton, G. E. 1991. Evaluation of adaptive mixtures of competing experts. In Advances in Neural Information Processing Systems 5, D. S. Touretzky, ed., Morgan Kaufmann, San Mateo, CA.
    • (1991) Advances in Neural Information Processing Systems 5 , vol.5
    • Nowlan, S.J.1    Hinton, G.E.2
  • 25
    • 0003241739 scopus 로고
    • Bumptrees for efficient function, constraint, and classification learning
    • R. P. Lippmann, J. E. Moody, and D. S. Touretzky, eds., Morgan Kaufmann, San Mateo, CA
    • Omohundro, P. 1991. Bumptrees for efficient function, constraint, and classification learning. In Advances in Neural Information Processing Systems 3, R. P. Lippmann, J. E. Moody, and D. S. Touretzky, eds., pp. 693-699. Morgan Kaufmann, San Mateo, CA.
    • (1991) Advances in Neural Information Processing Systems 3 , vol.3 , pp. 693-699
    • Omohundro, P.1
  • 26
    • 0025056697 scopus 로고
    • Regularization algorithms for learning that are equivalent to multilayer networks
    • Poggio, T., and Girosi, F. 1990. Regularization algorithms for learning that are equivalent to multilayer networks. Science 247.
    • (1990) Science , pp. 247
    • Poggio, T.1    Girosi, F.2
  • 27
    • 0000232749 scopus 로고
    • Learning with the self-organizing map
    • T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, eds., Elsevier Science Publishers B.V., North Holland
    • Ritter, H. 1991. Learning with the self-organizing map. In Artificial Neural Networks 1, T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, eds., pp. 357-364. Elsevier Science Publishers B.V., North Holland.
    • (1991) Artificial Neural Networks 1 , vol.1 , pp. 357-364
    • Ritter, H.1
  • 29
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart, D. E., Hinton, G. E., and Williams, R. J. 1986. Learning representations by back-propagating errors. Nature (London) 323, 533-536.
    • (1986) Nature (London) , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 30
    • 0014432211 scopus 로고
    • A two-dimensional interpolation function for irregularly spaced data
    • Shepard, D. 1968. A two-dimensional interpolation function for irregularly spaced data. Proc. 23rd Natl. Conf. ACM 517-523.
    • (1968) Proc. 23rd Natl. Conf. ACM , pp. 517-523
    • Shepard, D.1
  • 31
    • 0000796382 scopus 로고
    • Exploiting neurons with localized receptive fields to learn chaos
    • Stokbro, K., Umberger, D., and Hertz, J. 1990. Exploiting neurons with localized receptive fields to learn chaos. Complex Syst. 4, 603-622.
    • (1990) Complex Syst. , vol.4 , pp. 603-622
    • Stokbro, K.1    Umberger, D.2    Hertz, J.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.