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Volumn , Issue , 2007, Pages 14-19

An adaptive fuzzy regression model for the prediction of dichotomous response variables

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE FUZZY; ADAPTIVE MODELING; ADAPTIVE TECHNIQUES; COMPUTATIONAL SCIENCES; DATA SETS; EXPLANATORY POWER; FUZZY CONCEPTS; INTERNATIONAL CONFERENCES; LOGISTIC REGRESSION; LOGISTIC REGRESSION MODELING; MODEL BASED; ORAL CANCER; PREDICTIVE ABILITIES; RESPONSE VARIABLES;

EID: 48049093905     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCSA.2007.11     Document Type: Conference Paper
Times cited : (7)

References (11)
  • 1
    • 48049113508 scopus 로고    scopus 로고
    • A. F. Shapiro, Fuzzy Regression Models, ARC, 2005.
    • A. F. Shapiro, "Fuzzy Regression Models", ARC, 2005.
  • 2
    • 8744310100 scopus 로고    scopus 로고
    • A Quadratic Interval Logit Model for Forecasting Bankruptcy
    • F.M. Tseng and L. Lin, "A Quadratic Interval Logit Model for Forecasting Bankruptcy", Omega, Vol. 33, Issue 1, 2005, pp 85-91.
    • (2005) Omega , vol.33 , Issue.1 , pp. 85-91
    • Tseng, F.M.1    Lin, L.2
  • 4
    • 0001325256 scopus 로고    scopus 로고
    • Insight of a Fuzzy Regression Model
    • H.F Wang and R.C. Tsaur, "Insight of a Fuzzy Regression Model", Fuzzy Set and Systems, 2000, pp. 355-369.
    • (2000) Fuzzy Set and Systems , pp. 355-369
    • Wang, H.F.1    Tsaur, R.C.2
  • 6
    • 0030297904 scopus 로고    scopus 로고
    • Advantages and Disadvantages of Using Artificial Neural Networks Versus Logistic Regression for Predicting Medical Outcomes
    • J. V. Tu, "Advantages and Disadvantages of Using Artificial Neural Networks Versus Logistic Regression for Predicting Medical Outcomes", J Clin Epidemol, Vol 49, No 11, 1996, pp. 1225-1231,.
    • (1996) J Clin Epidemol , vol.49 , Issue.11 , pp. 1225-1231
    • Tu, J.V.1
  • 7
    • 48049092860 scopus 로고    scopus 로고
    • Machine Learning Approaches for Estimation of Prediction Interval for the Model Output
    • L. Durga. and P. Dimitri, "Machine Learning Approaches for Estimation of Prediction Interval for the Model Output", Neural Networks Special Issue, 2006, pp. 1-11.
    • (2006) Neural Networks Special Issue , pp. 1-11
    • Durga, L.1    Dimitri, P.2
  • 9
    • 48049109032 scopus 로고    scopus 로고
    • Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing
    • M. Nasiri et al., "Comparison of Statistical Regression, Fuzzy Regression and Artificial Neural Network Modeling Methodologies in Polyester Dyeing", Proceedings of 2005 International Conference for modeling, control and automation.
    • Proceedings of 2005 International Conference for modeling, control and automation
    • Nasiri, M.1
  • 10
    • 0043126911 scopus 로고    scopus 로고
    • Logistic Regression and Artificial Neural Network Classification Models: A Methodology Review
    • S. Dreiseitl and O. Machado, "Logistic Regression and Artificial Neural Network Classification Models: a Methodology Review", Journal of Biomedical Informatics, 35, 2003, pp352-359.
    • (2003) Journal of Biomedical Informatics , vol.35 , pp. 352-359
    • Dreiseitl, S.1    Machado, O.2
  • 11
    • 17844367854 scopus 로고    scopus 로고
    • Fuzzy Regression Method for Prediction and Control the Bead Width in the Robotic Arc Welding Process
    • Y. Xue et al., "Fuzzy Regression Method for Prediction and Control the Bead Width in the Robotic Arc Welding Process, Journal of Material Processing Technology, Vol. 164-165 2005, pp. 1134-1139
    • (2005) Journal of Material Processing Technology , vol.164-165 , pp. 1134-1139
    • Xue, Y.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.