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Volumn 30, Issue , 2001, Pages 27-53

Chapter 3 Multi-layer perceptrons and back-propagation learning

(1)  Poulton, Mary M a  

a NONE

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EID: 70350304054     PISSN: 09501401     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0950-1401(01)80017-3     Document Type: Article
Times cited : (11)

References (22)
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    • Fast-learning variations on back-propagation: An empirical study
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    • Fahlman S. Fast-learning variations on back-propagation: An empirical study. In: Touretzky D., Hinton G., and Sejnowski T. (Eds). Proceedings of the 1988 Connectionist Models Summer School. (Pittsburgh, 1988) (1989), Morgan-Kaufmann 38-51
    • (1989) Proceedings of the 1988 Connectionist Models Summer School , pp. 38-51
    • Fahlman, S.1
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    • The cascade-correlation learning architecture
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    • Fahlman S., and Lebiere C. The cascade-correlation learning architecture. In: Touretzky D. (Ed). Advances In Neural Information Processing Systems 1 (1990), Morgan-Kaufmann 524-532
    • (1990) Advances In Neural Information Processing Systems , vol.1 , pp. 524-532
    • Fahlman, S.1    Lebiere, C.2
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    • 0001234705 scopus 로고
    • Second order derivatives for network pruning: optimal brain surgeon
    • Hanson S., Cowan J., and Giles C. (Eds), Morgan-Kaufmann
    • Hassibi B., and Stork D. Second order derivatives for network pruning: optimal brain surgeon. In: Hanson S., Cowan J., and Giles C. (Eds). Advances in Neural Information Processing Systems 5 (1993), Morgan-Kaufmann 164-171
    • (1993) Advances in Neural Information Processing Systems , vol.5 , pp. 164-171
    • Hassibi, B.1    Stork, D.2
  • 9
    • 0024880831 scopus 로고
    • Multilayer feedforward neural networks are universal approximators
    • Hornik K., Stinchcombe M., and White H. Multilayer feedforward neural networks are universal approximators. Neural Networks 2 (1989) 359-366
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 10
    • 0024137490 scopus 로고
    • Increased rates of convergence through learning rate adaptation
    • Jacobs M. Increased rates of convergence through learning rate adaptation. Neural Networks 1 (1988) 295-307
    • (1988) Neural Networks , vol.1 , pp. 295-307
    • Jacobs, M.1
  • 14
    • 0004225001 scopus 로고
    • Learning-logic
    • April, Center for Computational Research in Economics and Management Science, MIT
    • April. Parker D. Learning-logic. Technical Report TR-47 (1985), Center for Computational Research in Economics and Management Science, MIT
    • (1985) Technical Report TR-47
    • Parker, D.1
  • 15
    • 0025056697 scopus 로고
    • Regularization algorithms for learning that are equivalent to multilayer networks
    • Poggio T., and Girosi F. Regularization algorithms for learning that are equivalent to multilayer networks. Science 247 (1990) 978-982
    • (1990) Science , vol.247 , pp. 978-982
    • Poggio, T.1    Girosi, F.2
  • 18
    • 0001336749 scopus 로고
    • Accelerated learning in layered neural networks
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    • (1988) Complex Systems , vol.2 , pp. 625-640
    • Solla, S.1    Levin, E.2    Fleisher, M.3
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    • Beyond regression: New tools for prediction and analysis in the behavioral sciences
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    • Werbos P. Beyond regression: New tools for prediction and analysis in the behavioral sciences. Ph.D. Dissertation (1974), Applied Math, Harvard University, Cambridge, MA
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    • Strategies for teaching layered networks classification tasks
    • Anderson D. (Ed), American Institute of Physics
    • Wittner B., and Denker J. Strategies for teaching layered networks classification tasks. In: Anderson D. (Ed). Neural Information Processing Systems, (Denver, 1987) (1988), American Institute of Physics 850-859
    • (1988) Neural Information Processing Systems, (Denver, 1987) , pp. 850-859
    • Wittner, B.1    Denker, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.