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Volumn 12, Issue 3, 2000, Pages 531-545

Nonlinear autoassociation is not equivalent to PCA

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ARTIFICIAL NEURAL NETWORK; NONLINEAR SYSTEM; NORMAL DISTRIBUTION; REPRODUCIBILITY; STATISTICAL MODEL;

EID: 0034153465     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976600300015691     Document Type: Article
Times cited : (149)

References (12)
  • 1
    • 0024774330 scopus 로고
    • Neural networks and principal component analysis: Learning from examples without local minima
    • Baldi, P., & Hornik, K. (1989). Neural networks and principal component analysis: Learning from examples without local minima. Neural Networks, 2, 53-58.
    • (1989) Neural Networks , vol.2 , pp. 53-58
    • Baldi, P.1    Hornik, K.2
  • 2
    • 0024220237 scopus 로고
    • Auto-association by multilayer perceptrons and singular value decomposition
    • Bourlard, H., & Kamp, Y. (1988). Auto-association by multilayer perceptrons and singular value decomposition. Biological Cybernetics, 59, 291-294.
    • (1988) Biological Cybernetics , vol.59 , pp. 291-294
    • Bourlard, H.1    Kamp, Y.2
  • 4
    • 0001821534 scopus 로고
    • PARSNIP: A connectionist network that learns natural language grammar from exposure to natural language sentences
    • Hanson, S. J., & Kegl, J. (1987). PARSNIP: A connectionist network that learns natural language grammar from exposure to natural language sentences. In Proceedings of the Ninth Annual Conference on Cognitive Science.
    • (1987) Proceedings of the Ninth Annual Conference on Cognitive Science
    • Hanson, S.J.1    Kegl, J.2
  • 5
    • 0007900786 scopus 로고    scopus 로고
    • Concept-learning in the absence of counter-examples: An autoassociation-based approach to classification
    • Dept. of Computer Science, Rutgers Univ.
    • Japkowicz, N. (1999). Concept-learning in the absence of counter-examples: An autoassociation-based approach to classification. Technical Report DCS-TR-390, Dept. of Computer Science, Rutgers Univ.
    • (1999) Technical Report DCS-TR-390
    • Japkowicz, N.1
  • 7
    • 0010504704 scopus 로고
    • How to solve the n bit encoder problem with just two hidden units
    • Kruglyak, L. (1990). How to solve the N bit encoder problem with just two hidden units. Neural Computation, 2, 399-401.
    • (1990) Neural Computation , vol.2 , pp. 399-401
    • Kruglyak, L.1
  • 10
    • 0039475239 scopus 로고
    • Construction of minimal N-2-N encoders for any N
    • Phatak, D. S., Choi, H., & Koren, I. (1993). Construction of minimal N-2-N encoders for any N. Neural Computation, 5, 783-794.
    • (1993) Neural Computation , vol.5 , pp. 783-794
    • Phatak, D.S.1    Choi, H.2    Koren, I.3
  • 11
    • 0000646059 scopus 로고
    • Learning internal representations by error propagation
    • D. E. Rumelhart & J. L. McClelland (Eds.), Cambridge, MA: MIT Press
    • Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing (pp. 318-364). Cambridge, MA: MIT Press.
    • (1986) Parallel Distributed Processing , pp. 318-364
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 12
    • 0040660558 scopus 로고
    • Programming neural nets to do spatial computations
    • N. Sharkey (Ed.), Norwood, NJ: Ablex
    • Zipser, D. (1989). Programming neural nets to do spatial computations. In N. Sharkey (Ed.), Models of cognition: A review of cognitive science. Norwood, NJ: Ablex.
    • (1989) Models of Cognition: A Review of Cognitive Science
    • Zipser, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.