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Volumn 3316, Issue , 2004, Pages 496-501

The most robust loss function for boosting

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BOOSTING; STATISTICS;

EID: 35048894955     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30499-9_76     Document Type: Article
Times cited : (16)

References (11)
  • 1
    • 0000889845 scopus 로고
    • Binary regression models for contaminated data
    • Copas, J. (1988). Binary regression models for contaminated data. J. Royal Statist. Soc. B., 50, 225-265.
    • (1988) J. Royal Statist. Soc. B. , vol.50 , pp. 225-265
    • Copas, J.1
  • 3
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119-139.
    • (1997) Journal of Computer and System Sciences , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 4
    • 0034164230 scopus 로고    scopus 로고
    • Additive logistic regression: A statistical view of boosting
    • Friedman, J. H., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28, 337-407.
    • (2000) Annals of Statistics , vol.28 , pp. 337-407
    • Friedman, J.H.1    Hastie, T.2    Tibshirani, R.3
  • 10
    • 1542291080 scopus 로고    scopus 로고
    • Robustifying adaboost by adding the naive error rate
    • Takenouchi, T., & Eguchi, S. (2004). Robustifying adaboost by adding the naive error rate. Neural Computation, vol. 16, num. 4, pp. 767 - 787.
    • (2004) Neural Computation , vol.16 , Issue.4 , pp. 767-787
    • Takenouchi, T.1    Eguchi, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.