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Volumn 115, Issue 1, 2000, Pages 121-140

Neural networks for soft decision making

Author keywords

Fuzzy arithmetic; Interval arithmetic; Neural networks; Reject option; Soft decision

Indexed keywords


EID: 0000993584     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0165-0114(99)00022-6     Document Type: Article
Times cited : (24)

References (24)
  • 2
    • 0026182828 scopus 로고
    • Fuzzy set representation of neural network classification boundary
    • Archer N.P., Wang S. Fuzzy set representation of neural network classification boundary. IEEE Trans. Systems Man Cybernet. 21:1991;735-742.
    • (1991) IEEE Trans. Systems Man Cybernet. , vol.21 , pp. 735-742
    • Archer, N.P.1    Wang, S.2
  • 5
    • 84937744538 scopus 로고
    • An optimum character recognition system using decision function
    • Chow C.K. An optimum character recognition system using decision function. IRE Trans. Electron. Comput. 6:1957;247-254.
    • (1957) IRE Trans. Electron. Comput. , vol.6 , pp. 247-254
    • Chow, C.K.1
  • 6
    • 0014710323 scopus 로고
    • An optimum recognition error and reject tradeoff
    • Chow C.K. An optimum recognition error and reject tradeoff. IEEE Trans. Inform. Theory. 16:1970;41-46.
    • (1970) IEEE Trans. Inform. Theory , vol.16 , pp. 41-46
    • Chow, C.K.1
  • 8
    • 0000764772 scopus 로고
    • The use of multiple measurements in taxonomic problems
    • Fisher R.A. The use of multiple measurements in taxonomic problems. Ann. Eugenics. 7:1936;179-188.
    • (1936) Ann. Eugenics , vol.7 , pp. 179-188
    • Fisher, R.A.1
  • 9
    • 0039194525 scopus 로고    scopus 로고
    • Learning rejection thresholds for a class of fuzzy classifiers for possibilistic clustered noisy data
    • Prague
    • C. Frelicot, Learning rejection thresholds for a class of fuzzy classifiers for possibilistic clustered noisy data, Proc. 7th IFSA World Congress III, Prague, 1997, pp. 111-116.
    • (1997) Proc. 7th IFSA World Congress , vol.3 , pp. 111-116
    • Frelicot, C.1
  • 10
    • 0028480740 scopus 로고
    • Classification by fuzzy integral: Performance and tests
    • Grabisch M., Nicolas J.-M. Classification by fuzzy integral: performance and tests. Fuzzy Sets and Systems. 65:1994;255-271.
    • (1994) Fuzzy Sets and Systems , vol.65 , pp. 255-271
    • Grabisch, M.1    Nicolas, J.-M.2
  • 12
    • 38249012340 scopus 로고
    • Possibility and necessity pattern classification using neural networks
    • Ishibuchi H., Fujioka R., Tanaka H. Possibility and necessity pattern classification using neural networks. Fuzzy Sets and Systems. 48:1992;331-340.
    • (1992) Fuzzy Sets and Systems , vol.48 , pp. 331-340
    • Ishibuchi, H.1    Fujioka, R.2    Tanaka, H.3
  • 19
    • 0039871725 scopus 로고
    • Approximate pattern classification using neural networks
    • R. Lowen, & M. Roubens. Dordrecht, Netherlands: Kluwer Academic
    • Ishibuchi H., Tanaka H. Approximate pattern classification using neural networks. Lowen R., Roubens M. Fuzzy Logic. State of the Art:1993;225-236 Kluwer Academic, Dordrecht, Netherlands.
    • (1993) Fuzzy Logic: State of the Art , pp. 225-236
    • Ishibuchi, H.1    Tanaka, H.2
  • 21
    • 0031142488 scopus 로고    scopus 로고
    • Quantum neural networks (QNN's): Inherently fuzzy feedforward neural networks
    • Purushothaman G., Karayiannis N.B. Quantum neural networks (QNN's). inherently fuzzy feedforward neural networks IEEE Trans. Neural Networks. 8:1997;679-693.
    • (1997) IEEE Trans. Neural Networks , vol.8 , pp. 679-693
    • Purushothaman, G.1    Karayiannis, N.B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.