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Volumn 32, Issue 1, 2004, Pages 1-11

The 2002 wald memorial lectures population theory for boosting ensembles

Author keywords

AdaBoost; Bayes risk; Trees

Indexed keywords


EID: 14644395930     PISSN: 00905364     EISSN: None     Source Type: Journal    
DOI: 10.1214/aos/1079120126     Document Type: Article
Times cited : (79)

References (20)
  • 1
    • 0032645080 scopus 로고    scopus 로고
    • An empirical comparison of voting classification algorithms: Bagging, boosting and variants
    • BAUER, E. and KOHAVI, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 36 105-139.
    • (1999) Machine Learning , vol.36 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 2
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • BREIMAN, L. (1996). Bagging predictors. Machine Learning 24 123-140.
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 3
    • 0004198448 scopus 로고    scopus 로고
    • Arcing the edge
    • Dept. Statistics, Univ. California, Berkeley
    • BREIMAN, L. (1997). Arcing the edge. Technical Report 486, Dept. Statistics, Univ. California, Berkeley. Available at www.stat.berkeley.edu.
    • (1997) Technical Report , vol.486
    • Breiman, L.1
  • 4
    • 0346786584 scopus 로고    scopus 로고
    • Arcing classifiers
    • BREIMAN, L. (1998). Arcing classifiers (with discussion). Ann. Statist. 26 801-849.
    • (1998) Ann. Statist. , vol.26 , pp. 801-849
    • Breiman, L.1
  • 5
    • 0000275022 scopus 로고    scopus 로고
    • Prediction games and arcing algorithms
    • BREIMAN, L. (1999). Prediction games and arcing algorithms. Neural Computation 11 1493-1517.
    • (1999) Neural Computation , vol.11 , pp. 1493-1517
    • Breiman, L.1
  • 6
    • 0013228784 scopus 로고    scopus 로고
    • Some infinite theory for predictor ensembles
    • Dept. Statistics, Univ. California, Berkeley
    • BREIMAN, L. (2000). Some infinite theory for predictor ensembles. Technical Report 577, Dept. Statistics, Univ. California, Berkeley.
    • (2000) Technical Report , vol.577
    • Breiman, L.1
  • 8
    • 0034250160 scopus 로고    scopus 로고
    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization
    • DIETTERICH, T. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Machine Learning 40 139-157.
    • (2000) Machine Learning , vol.40 , pp. 139-157
    • Dietterich, T.1
  • 13
    • 0034164230 scopus 로고    scopus 로고
    • Additive logistic regression: A statistical view of boosting
    • FRIEDMAN, J., HASTIE, T. and TIBSHIRANI, R. (2000). Additive logistic regression: A statistical view of boosting (with discussion). Ann. Statist. 28 337-407.
    • (2000) Ann. Statist. , vol.28 , pp. 337-407
    • Friedman, J.1    Hastie, T.2    Tibshirani, R.3
  • 14
    • 26444545593 scopus 로고    scopus 로고
    • Process consistency for AdaBoost
    • JIANG, W. (2004). Process consistency for AdaBoost. Ann. Statist. 32 13-29.
    • (2004) Ann. Statist. , vol.32 , pp. 13-29
    • Jiang, W.1
  • 15
    • 9444269961 scopus 로고    scopus 로고
    • On the Bayes-risk consistency of regularized boosting methods
    • LUGOSI, G. and VAYATIS, N. (2004). On the Bayes-risk consistency of regularized boosting methods. Ann. Statist. 32 30-55.
    • (2004) Ann. Statist. , vol.32 , pp. 30-55
    • Lugosi, G.1    Vayatis, N.2
  • 17
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • SCHAPIRE, R., FREUND, Y., BARTLETT, P. and LEE, W. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Ann. Statist. 26 1651-1686.
    • (1998) Ann. Statist. , vol.26 , pp. 1651-1686
    • Schapire, R.1    Freund, Y.2    Bartlett, P.3    Lee, W.4
  • 18
    • 0033281701 scopus 로고    scopus 로고
    • Improved boosting algorithms using confidence-rated predictions
    • SCHAPIRE, R. and SINGER, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine Learning 37 297-336.
    • (1999) Machine Learning , vol.37 , pp. 297-336
    • Schapire, R.1    Singer, Y.2
  • 20
    • 1542307688 scopus 로고    scopus 로고
    • Boosting with early stopping: Convergence and consistency
    • Dept. Statistics, Univ. California, Berkeley
    • ZHANG, T. and YU, B. (2003). Boosting with early stopping: Convergence and consistency. Technical Report 635, Dept. Statistics, Univ. California, Berkeley. Available from www.stat.berkeley.edu/~binyu/publications.html.
    • (2003) Technical Report , vol.635
    • Zhang, T.1    Yu, B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.