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Volumn 1, Issue , 2000, Pages 505-509

Controlling asymmetric errors in neuro-fuzzy classification

Author keywords

Aerial images; Asymmetric errors; Neuro fuzzy classifiers; Unbalanced data

Indexed keywords

AERIAL IMAGES; ASYMMETRIC ERRORS; CLASS PROBABILITIES; MISCLASSIFICATIONS; NEURO FUZZY CLASSIFICATION; NEURO-FUZZY CLASSIFIERS; NEUROFUZZY SYSTEM; UNBALANCED DATA;

EID: 0002090593     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/335603.335933     Document Type: Conference Paper
Times cited : (6)

References (13)
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  • 3
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  • 4
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    • Some approaches to improve the interpretability of neuro-fuzzy classifiers
    • A. Klose, A. Nürnberger, and D. Nauck. Some approaches to improve the interpretability of neuro-fuzzy classifiers. In Proc. EUFIT'98, pages 629-633, 1998.
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    • Klose, A.1    Nürnberger, A.2    Nauck, D.3
  • 5
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    • BPI: Ein Blackboard-basiertes Produktionssystem für die automatische Bildauswertung
    • G. Hartmann, editor, Springer Verlag, Berlin
    • K. Lütjen. BPI: Ein Blackboard-basiertes Produktionssystem für die automatische Bildauswertung. In G. Hartmann, editor, Mustererkennung 1986, 8. DAGM-Symposium, pages 164-168. Springer Verlag, Berlin, 1986.
    • (1986) Mustererkennung 1986, 8. DAGM-Symposium , pp. 164-168
    • Lütjen, K.1
  • 7
    • 0029191595 scopus 로고
    • NEFCLASS - A neuro-fuzzy approach for the classification of data
    • K. M. George, J. H. Carrol, E. Deaton, D. Oppenheim, and J. High-tower, editors, ACM Press, New York, Feb.
    • D. Nauck and R. Kruse. NEFCLASS - a neuro-fuzzy approach for the classification of data. In K. M. George, J. H. Carrol, E. Deaton, D. Oppenheim, and J. High-tower, editors, Applied Computing 1995. Proc. 1995 ACM Symposium on Applied Computing, Nashville, Feb. 26-28, pages 461-465. ACM Press, New York, Feb. 1995.
    • (1995) Applied Computing 1995. Proc. 1995 ACM Symposium on Applied Computing, Nashville, Feb. 26-28 , pp. 461-465
    • Nauck, D.1    Kruse, R.2
  • 8
    • 0001703957 scopus 로고    scopus 로고
    • A neuro-fuzzy method to learn fuzzy classification rules from data
    • D. Nauck and R. Kruse. A neuro-fuzzy method to learn fuzzy classification rules from data. Fuzzy Sets and Systems, 89:277-288, 1997, (Pubitemid 127699103)
    • (1997) Fuzzy Sets and Systems , vol.89 , Issue.3 , pp. 277-288
    • Nauck, D.1    Kruse, R.2
  • 12
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    • Generation and improvement of fuzzy classifiers with incremental learning using fuzzy rulenet
    • K. M. George, J. H. Carrol, E. Deaton, D. Oppenheim, and J. Hightower, editors, ACM Press, New York, Feb.
    • N. Tschichold-Gürinan. Generation and improvement of fuzzy classifiers with incremental learning using fuzzy rulenet. In K. M. George, J. H. Carrol, E. Deaton, D. Oppenheim, and J. Hightower, editors, Applied Computing 1995. Proc. 1995 ACM Symposium on Applied Computing, Nashville, Feb. 26-28, pages 466-470. ACM Press, New York, Feb. 1995.
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  • 13
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    • Generating fuzzy rules by learning from examples
    • L.-X. Wang and J. M. Mendel. Generating fuzzy rules by learning from examples. IEEE Trans. Syst., Man, Cybern., 22(6):1414-1427, 1992.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.