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Volumn 32, Issue 1, 2004, Pages 30-55

On the Bayes-risk consistency of regularized boosting methods

Author keywords

Bayes risk consistency; Boosting; Classification; Convex cost functions; Empirical processes; Penalized model selection; Smoothing parameter

Indexed keywords


EID: 9444269961     PISSN: 00905364     EISSN: None     Source Type: Journal    
DOI: 10.1214/aos/1079120129     Document Type: Article
Times cited : (169)

References (33)
  • 3
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • BREIMAN, L. (1996a). Bagging predictors. Machine Learning 24 123-140.
    • (1996) Machine Learning , vol.24 , pp. 123-140
    • Breiman, L.1
  • 4
    • 0003619255 scopus 로고    scopus 로고
    • Bias, variance, and arcing classifiers
    • Dept. Statistics, Univ. California, Berkeley
    • BREIMAN, L. (1996b). Bias, variance, and arcing classifiers. Technical Report 460, Dept. Statistics, Univ. California, Berkeley.
    • (1996) Technical Report , vol.460
    • Breiman, L.1
  • 5
    • 0004198448 scopus 로고    scopus 로고
    • Arcing the edge
    • Dept. Statistics, Univ. California, Berkeley
    • BREIMAN, L. (1997a). Arcing the edge. Technical Report 486, Dept. Statistics, Univ. California, Berkeley.
    • (1997) Technical Report , vol.486
    • Breiman, L.1
  • 6
    • 0007325881 scopus 로고    scopus 로고
    • Pasting bites together for prediction in large data sets and on-line
    • Dept. Statistics, Univ. California, Berkeley
    • BREIMAN, L. (1997b). Pasting bites together for prediction in large data sets and on-line. Technical report, Dept. Statistics, Univ. California, Berkeley.
    • (1997) Technical Report
    • Breiman, L.1
  • 7
    • 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
  • 8
    • 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
  • 9
    • 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
  • 10
    • 0041612447 scopus 로고    scopus 로고
    • Discussion of "Additive logistic regression: A statistical view of boosting" by J. Friedman, T. Hastie and R. Tibshirani
    • J. Friedman, T. Hastie and R. Tibshirani.
    • BÜHLMANN, P. and Yu, B. (2000). Discussion of "Additive logistic regression: A statistical view of boosting" by J. Friedman, T. Hastie and R. Tibshirani. Ann. Statist. 28 377-386.
    • (2000) Ann. Statist. , vol.28 , pp. 377-386
    • Bühlmann, P.1    Yu, B.2
  • 12
    • 0036643072 scopus 로고    scopus 로고
    • Logistic regression, AdaBoost and Bregman distances
    • COLLINS, M., SCHAPIRE, R. E. and SINGER, Y. (2002). Logistic regression, AdaBoost and Bregman distances. Machine Learning 48 253-285.
    • (2002) Machine Learning , vol.48 , pp. 253-285
    • Collins, M.1    Schapire, R.E.2    Singer, Y.3
  • 15
    • 58149321460 scopus 로고
    • Boosting a weak learning algorithm by majority
    • FREUND, Y. (1995). Boosting a weak learning algorithm by majority. Inform, and Comput. 121 256-285.
    • (1995) Inform, and Comput. , vol.121 , pp. 256-285
    • Freund, Y.1
  • 19
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of on-line learning and an application to boosting
    • FREUND, Y. and SCHAPIRE, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. System Sci. 55 119-139.
    • (1997) J. Comput. System Sci. , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 20
    • 26844494532 scopus 로고    scopus 로고
    • Discussion of "Additive logistic regression: A statistical view of boosting," by J. Friedman, T. Hastie and R. Tibshirani
    • FREUND, Y. and SCHAPIRE, R. E. (2000). Discussion of "Additive logistic regression: A statistical view of boosting," by J. Friedman, T. Hastie and R. Tibshirani. Ann. Statist. 28 391-393.
    • (2000) Ann. Statist. , vol.28 , pp. 391-393
    • Freund, Y.1    Schapire, R.E.2
  • 21
    • 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
  • 22
    • 0041372893 scopus 로고    scopus 로고
    • Some theoretical aspects of boosting in the presence of noisy data
    • Morgan Kaufmann, San Francisco
    • JIANG, W. (2001). Some theoretical aspects of boosting in the presence of noisy data. In Proc. 18th International Conference on Machine Learning (ICML-2001) 234-241. Morgan Kaufmann, San Francisco.
    • (2001) Proc. 18th International Conference on Machine Learning (ICML-2001) , pp. 234-241
    • Jiang, W.1
  • 23
    • 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
  • 24
    • 0036104545 scopus 로고    scopus 로고
    • Empirical margin distributions and bounding the generalization error of combined classifiers
    • KOLTCHINSKII, V. and PANCHENKO, D. (2002). Empirical margin distributions and bounding the generalization error of combined classifiers. Ann. Statist. 30 1-50.
    • (2002) Ann. Statist. , vol.30 , pp. 1-50
    • Koltchinskii, V.1    Panchenko, D.2
  • 29
    • 0002550596 scopus 로고    scopus 로고
    • Functional gradient techniques for combining hypotheses
    • (A. J. Smola, P. L. Bartlett, B. Schölkopf and D. Schuurmans, eds.). MIT Press, Cambridge, MA
    • MASON, L., BAXTER, J., BARTLETT, P. L. and FREAN, M. (2000). Functional gradient techniques for combining hypotheses. In Advances in Large Margin Classifiers (A. J. Smola, P. L. Bartlett, B. Schölkopf and D. Schuurmans, eds.) 221-247. MIT Press, Cambridge, MA.
    • (2000) Advances in Large Margin Classifiers , pp. 221-247
    • Mason, L.1    Baxter, J.2    Bartlett, P.L.3    Frean, M.4
  • 30
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • SCHAPIRE, R. E. (1990). The strength of weak learnability. Machine Learning 5 197-227.
    • (1990) Machine Learning , vol.5 , pp. 197-227
    • Schapire, R.E.1
  • 31
    • 0032280519 scopus 로고    scopus 로고
    • Boosting the margin: A new explanation for the effectiveness of voting methods
    • SCHAPIRE, R. E., FREUND, Y., BARTLETT, P. and LEE, W. S. (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.E.1    Freund, Y.2    Bartlett, P.3    Lee, W.S.4
  • 33
    • 4644257995 scopus 로고    scopus 로고
    • Statistical behavior and consistency of classification methods based on convex risk minimization
    • ZHANG, T. (2004). Statistical behavior and consistency of classification methods based on convex risk minimization. Ann. Statist. 32 56-85.
    • (2004) Ann. Statist. , vol.32 , pp. 56-85
    • Zhang, T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.