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Volumn 4756 LNCS, Issue , 2007, Pages 427-436

Robust alternating AdaBoost

Author keywords

AdaBoost; Machine ensembles; Robust learning algorithms

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA REDUCTION; PROBLEM SOLVING;

EID: 38449112056     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: None     Document Type: Conference Paper
Times cited : (15)

References (9)
  • 1
    • 9444245013 scopus 로고    scopus 로고
    • Allende, H., Nanculef, R., Salas, R.: Bootstrapping neural networks. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), 2972, pp. 813-822. Springer, Heidelberg (2004)
    • Allende, H., Nanculef, R., Salas, R.: Bootstrapping neural networks. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 813-822. Springer, Heidelberg (2004)
  • 2
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
    • Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36(1-2), 105-139 (1999)
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 4
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L.: Bagging predictors. Machine Learning 24(2), 123-140 (1996)
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 6
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119-139 (1997)
    • (1997) Journal of Computer and System Sciences , vol.55 , Issue.1 , pp. 119-139
    • Freund, Y.1    Schapire, R.2
  • 7
    • 35048894955 scopus 로고    scopus 로고
    • The most robust loss function for boosting
    • Pal, N.R, Kasabov, N, Mudi, R.K, Pal, S, Parui, S.K, eds, ICONIP 2004, Springer, Heidelberg
    • Kanamori, T., Takenouchi, T., Eguchi, S., Murata, N.: The most robust loss function for boosting. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 496-501. Springer, Heidelberg (2004)
    • (2004) LNCS , vol.3316 , pp. 496-501
    • Kanamori, T.1    Takenouchi, T.2    Eguchi, S.3    Murata, N.4
  • 8
    • 84861973487 scopus 로고    scopus 로고
    • Kuncheva, L., Whitaker, C.: Using diversity with three variants of boosting: Aggressive, conservative and inverse. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, 2364, pp. 81-90. Springer, Heidelberg (2002)
    • Kuncheva, L., Whitaker, C.: Using diversity with three variants of boosting: Aggressive, conservative and inverse. In: Roli, F., Kittler, J. (eds.) MCS 2002. LNCS, vol. 2364, pp. 81-90. Springer, Heidelberg (2002)
  • 9
    • 0021518106 scopus 로고
    • A theory of the learnable
    • Valiant, L.G.: A theory of the learnable. Communications of the ACM 27(11), 1134-1142 (1984)
    • (1984) Communications of the ACM , vol.27 , Issue.11 , pp. 1134-1142
    • Valiant, L.G.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.