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Volumn 12, Issue 10, 2000, Pages 2405-2426

Generalization and selection of examples in feedforward neural networks

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; FEEDBACK SYSTEM; NERVE CELL; PHYSIOLOGY;

EID: 0034293035     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976600300014999     Document Type: Article
Times cited : (26)

References (18)
  • 1
    • 0025897859 scopus 로고
    • Neural net algorithms that learn in polynomial time from examples and queries
    • Baum, E. B. (1991). Neural net algorithms that learn in polynomial time from examples and queries. IEEE Transactions on Neural Networks, 2(1), 5.
    • (1991) IEEE Transactions on Neural Networks , vol.2 , Issue.1 , pp. 5
    • Baum, E.B.1
  • 2
    • 0001160588 scopus 로고
    • What net size gives valid generalization?
    • Baum, E. B., & Haussler, D. (1989). What net size gives valid generalization? Neural Computation, 2(1), 151.
    • (1989) Neural Computation , vol.2 , Issue.1 , pp. 151
    • Baum, E.B.1    Haussler, D.2
  • 4
    • 0010581965 scopus 로고
    • Arithmetic perceptrons
    • Cannas, S. A. (1995). Arithmetic perceptrons. Neural Computation, 7(1), 173.
    • (1995) Neural Computation , vol.7 , Issue.1 , pp. 173
    • Cannas, S.A.1
  • 5
    • 0030221433 scopus 로고    scopus 로고
    • Neural network exploration using optimal experiment design
    • Cohn, D. (1996). Neural network exploration using optimal experiment design. Neural Networks, 9(6), 1071.
    • (1996) Neural Networks , vol.9 , Issue.6 , pp. 1071
    • Cohn, D.1
  • 6
    • 0028424239 scopus 로고
    • Improving generalization with active learning
    • Cohn, D., Atlas, L., & Ladner, R. (1994). Improving generalization with active learning. Machine Learning, 15(2), 201.
    • (1994) Machine Learning , vol.15 , Issue.2 , pp. 201
    • Cohn, D.1    Atlas, L.2    Ladner, R.3
  • 7
    • 21844526900 scopus 로고
    • On-line learning in the committee machine
    • Copelli, M., & Caticha, N. (1995). On-line learning in the committee machine. J. Phys. A: Math. Gen., 28, 1615.
    • (1995) J. Phys. A: Math. Gen. , vol.28 , pp. 1615
    • Copelli, M.1    Caticha, N.2
  • 8
    • 0031911201 scopus 로고    scopus 로고
    • Solving arithmetic problems using feed-forward neural networks
    • Franco, L., & Cannas, S. A. (1998). Solving arithmetic problems using feed-forward neural networks. Neurocomputing, 18, 61-79.
    • (1998) Neurocomputing , vol.18 , pp. 61-79
    • Franco, L.1    Cannas, S.A.2
  • 9
    • 0028495825 scopus 로고
    • Learning nonoverlapping perceptron networks from examples and membership queries
    • Hancock, T. R., Golea, M., & Marchand, M. (1994). Learning nonoverlapping perceptron networks from examples and membership queries. Machine Learning, 16, 161.
    • (1994) Machine Learning , vol.16 , pp. 161
    • Hancock, T.R.1    Golea, M.2    Marchand, M.3
  • 12
    • 21344469705 scopus 로고    scopus 로고
    • Selection of examples for a linear classifier
    • Jung, G., & Opper, M. (1996). Selection of examples for a linear classifier. J. Phys. A: Math. Gen., 29(7), 1367.
    • (1996) J. Phys. A: Math. Gen. , vol.29 , Issue.7 , pp. 1367
    • Jung, G.1    Opper, M.2
  • 13
    • 84956214804 scopus 로고
    • Improving a network generalization ability by selecting examples
    • Kinzel, W., & Ruján, P. (1990). Improving a network generalization ability by selecting examples. Europhysics Letters, 13(5), 473.
    • (1990) Europhysics Letters , vol.13 , Issue.5 , pp. 473
    • Kinzel, W.1    Ruján, P.2
  • 16
  • 18
    • 0006688976 scopus 로고
    • Scaling relationships in back-propagation learning: Dependence on predicate order
    • Urbana-Champaign, IL: Center for Complex System Research
    • Tessauro, G., & Janssens, R. (1988). Scaling relationships in back-propagation learning: Dependence on predicate order (Tech. Rep. No. CCSR-88-1). Urbana-Champaign, IL: Center for Complex System Research.
    • (1988) Tech. Rep. No. CCSR-88-1
    • Tessauro, G.1    Janssens, R.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.