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Volumn 9, Issue 1, 1997, Pages 205-225

Extracting rules from neural networks by pruning and hidden-unit splitting

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; EXON; INTRON; LEARNING; NERVE CELL; NUCLEOTIDE SEQUENCE; PHYSIOLOGY; REPRODUCIBILITY; SOUND;

EID: 0030631792     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/neco.1997.9.1.205     Document Type: Article
Times cited : (122)

References (16)
  • 1
    • 0005995503 scopus 로고
    • GDS: Gradient descent generation of symbolic classification rules
    • Morgan Kaufmann, San Mateo, CA
    • Blassig, R. 1994. GDS: Gradient descent generation of symbolic classification rules. In Advances in Neural Information Processing Systems, Vol. 6, pp. 1093-1100. Morgan Kaufmann, San Mateo, CA.
    • (1994) Advances in Neural Information Processing Systems , vol.6 , pp. 1093-1100
    • Blassig, R.1
  • 3
    • 0027242791 scopus 로고
    • Backpropagation neural nets with one and two hidden layers
    • de Villiers, J., and Barnard, E. 1992. Backpropagation neural nets with one and two hidden layers. IEEE Trans. on Neural Networks 4(1), 136-141.
    • (1992) IEEE Trans. on Neural Networks , vol.4 , Issue.1 , pp. 136-141
    • De Villiers, J.1    Barnard, E.2
  • 4
    • 84920183623 scopus 로고
    • Rule learning by searching on adapted nets
    • AAAI Press/MIT Press, Menlo Park, CA
    • Fu, L. 1991. Rule learning by searching on adapted nets. In Proc. of the Ninth National Conference on Artificial Intelligence, pp. 590-595. AAAI Press/MIT Press, Menlo Park, CA.
    • (1991) Proc. of the Ninth National Conference on Artificial Intelligence , pp. 590-595
    • Fu, L.1
  • 5
    • 0023843391 scopus 로고
    • Analysis of hidden units in a layered network trained to classify sonar targets
    • Gorman, R. P., and Sejnowski, T. J. 1988. Analysis of hidden units in a layered network trained to classify sonar targets. Neural Networks 1, 75-89.
    • (1988) Neural Networks , vol.1 , pp. 75-89
    • Gorman, R.P.1    Sejnowski, T.J.2
  • 6
    • 0025751820 scopus 로고
    • Approximation capabilities of multilayer feedforward neural networks
    • Hornik, K. 1991. Approximation capabilities of multilayer feedforward neural networks. Neural Networks 4, 251-257.
    • (1991) Neural Networks , vol.4 , pp. 251-257
    • Hornik, K.1
  • 7
  • 8
    • 85170186233 scopus 로고
    • Application of neural networks and other machine learning algorithms to DNA sequence analysis
    • Addison-Wesley, Redwood City, CA
    • Lapedes, A., Barnes, C., Burks, C., Farber, R., and Sirotkin, K. 1989. Application of neural networks and other machine learning algorithms to DNA sequence analysis. In Computers and DNA, pp. 157-182, Addison-Wesley, Redwood City, CA.
    • (1989) Computers and DNA , pp. 157-182
    • Lapedes, A.1    Barnes, C.2    Burks, C.3    Farber, R.4    Sirotkin, K.5
  • 13
    • 0030633575 scopus 로고    scopus 로고
    • A penalty-function approach for pruning feedforward neural networks
    • Setiono, R. 1997. A penalty-function approach for pruning feedforward neural networks. Neural Computation 9, 185-204.
    • (1997) Neural Computation , vol.9 , pp. 185-204
    • Setiono, R.1
  • 15
    • 0003276432 scopus 로고
    • Extracting rules from artificial neural networks with distributed representations
    • MIT Press, Cambridge, MA
    • Thrun, S. 1995. Extracting rules from artificial neural networks with distributed representations. In Advances in Neural Information Processing Systems, Vol. 7. MIT Press, Cambridge, MA.
    • (1995) Advances in Neural Information Processing Systems , vol.7
    • Thrun, S.1
  • 16
    • 0027678679 scopus 로고
    • Extracting refined rules from knowledge-based neural networks
    • Towell, G. G., and Shavlik, J. W. 1993. Extracting refined rules from knowledge-based neural networks. Machine Learning 13(1), 71-101.
    • (1993) Machine Learning , vol.13 , Issue.1 , pp. 71-101
    • Towell, G.G.1    Shavlik, J.W.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.