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Volumn 382, Issue , 2009, Pages

GAODE and HAODE : Two proposals based on AODE to deal with continuous variables

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN CLASSIFIER; CONTINUOUS VARIABLES; DATA SETS; ERROR RATE; GAUSSIAN NETWORKS; GAUSSIANS; MULTINOMIAL DISTRIBUTIONS; UNIVARIATE;

EID: 70049106294     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1553374.1553414     Document Type: Conference Paper
Times cited : (19)

References (24)
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  • 5
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    • Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res., 7, 1-30.
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  • 6
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    • Beyond independence: Conditions for the optimality of the simple Bayesian classifier
    • Domingos, P., & Pazzani, M. (1996). Beyond independence: Conditions for the optimality of the simple Bayesian classifier. Proc. of the 13th Int. Conf. on Machine Learning (pp. 105-112).
    • (1996) Proc. of the 13th Int. Conf. on Machine Learning , pp. 105-112
    • Domingos, P.1    Pazzani, M.2
  • 8
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    • 58149287952 scopus 로고    scopus 로고
    • An extension on "statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons
    • Garcìa, S., & Herrera, F. (2009). An extension on "statistical comparisons of classifiers over multiple data sets" for all pairwise comparisons. J. Mach. Learn. Res., 9, 2677-2694.
    • (2009) J. Mach. Learn. Res , vol.9 , pp. 2677-2694
    • Garcìa, S.1    Herrera, F.2
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    • 0002610991 scopus 로고    scopus 로고
    • Learning augmented Bayesian classifiers: A comparison of distributionbased and classification-based approaches
    • Keogh, E., & Pazzani, M. (1999). Learning augmented Bayesian classifiers: A comparison of distributionbased and classification-based approaches. Proc. of the 7th Int. Workshop on AI and Statistics (pp. 225-230).
    • (1999) Proc. of the 7th Int. Workshop on AI and Statistics , pp. 225-230
    • Keogh, E.1    Pazzani, M.2
  • 13
    • 0004069443 scopus 로고    scopus 로고
    • Optimization by learning and simulation of Bayesian and Gaussian networks
    • University of the Basque Country
    • Larrañaga, P., Etxeberria, R., Lozano, J., & Peña, J. M. (1999). Optimization by learning and simulation of Bayesian and Gaussian networks (Technical Report). University of the Basque Country.
    • (1999) Technical Report
    • Larrañaga, P.1    Etxeberria, R.2    Lozano, J.3    Peña, J.M.4
  • 14
    • 0041377763 scopus 로고    scopus 로고
    • Stable local computation with conditional Gaussian distributions
    • Lauritzen, S. L., & Jensen, F. (2001). Stable local computation with conditional Gaussian distributions. Statistics and Computing, 11, 191-203.
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    • Lauritzen, S.L.1    Jensen, F.2
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    • Supervised classification with conditional gaussian networks: Increasing the structure complexity from naive Bayes
    • Pérez, A., Larrañaga, P., & Inza, I. (2006). Supervised classification with conditional gaussian networks: Increasing the structure complexity from naive Bayes. Int. J. Approx. Reasoning, 43, 1-25.
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    • Not So Naive Bayes: Aggregating One-Dependence Estimators
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.