Early stopping in L2 Boosting

Computational Statistics and Data Analysis

Volumn 54, Issue 10, 2010, Pages 2203-2213

  Ivan Chang, Yuan Chin ^a   Huang, Yufen ^b   Huang, Yu Pai ^b  

^a INSTITUTE OF STATISTICAL SCIENCE   (Taiwan)

^b NATIONAL CHUNG CHENG UNIVERSITY   (Taiwan)

Author keywords

AICc; BIC; Change point detection method; GMDL; L2 Boosting; LogitBoost; Stopping rule

Indexed keywords

ADABOOST; BIOINFORMATICS APPLICATIONS; BOOSTING ALGORITHM; CHANGE POINT DETECTION; COMPUTATIONAL SAVINGS; DATA EXAMPLES; EARLY STOPPING; LOGITBOOST; MODEL SELECTION CRITERIA; SIMULATION STUDIES; STOPPING RULE; TRAINING DATA; TWO-CLASS CLASSIFICATION PROBLEMS;

ADAPTIVE BOOSTING; BIOINFORMATICS; COMPUTER AIDED DIAGNOSIS;

MEDICAL PROBLEMS;

EID: 77955272634     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2010.03.024     Document Type: Article

References (22)

1. H. Akaike Statistical predictor identification Annals of the Institute of Statistical Mathematics 22 1970 203 217
2. Akaike, H., 1973. Information theory and an extension of the maximum likelihood principle. In: Proc. 2nd Int. Symp. Inform. Therory, Problems of Control and Inform. Theory, pp. 559583.
3. H. Akaike A new look at the statistical model identification IEEE Transactions on Automatic Control 19 1974 716 723
4. R.M. Blair, H. Fang, W.S. Branham, B.S Hass, S.L. Dial, C.L. Moland, W. Tong, L. Shi, R.G. Perkins, and D.M. Sheehan The estrogen receptor relative binding affinities of 188 natural and xenochemicals: structural diversity of ligands Toxicological Science 54 2000 138 153
5. W.S. Branham, S.L. Dial, C.L. Moland, B.S Hass, R.M. Blair, H. Fang, L. Shi, W. Tong, R.G. Perkins, and D.M. Sheehan Phytoestrogens and mycoestrogens bind to the rat uterine estrogen receptor The Journal of Nutrition 2002 658 664
6. P. Bhlmann, and B. Yu Boosting with the L 2 loss: regression and classification Journal of the American Statistical Association 98 2003 324 339
7. Bhlmann, P., Yu, B., 2005. Boosting, model selection, lasso and nonnegative garrote. Research Report No 127. Seminar fr Statistik. Eidgenssische Technische Hochschule, ETH, Zentrum, Switzerland.
8. M. Collins, R.E. Schapire, and Y. Singer Logistic regression, AdaBoost and Bregman distances Machine Learning 48 2002 253 285
9. Freund, Y., Schapire, R.E., 1996a. Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148156.
10. Freund, Y., Schapire, R.E., 1996b. Game theory, on-line prediction and boosting. In: Computational Learning Theory, pp. 325332.
11. J. Friedman Greedy function approximation: a gradient boosting machine Annals of Statistics 29 2001 1189 1232
12. Friedman, J., Hastie, T., Tibishirani, R., 2000. Additive logistic regression: a statistical view of boosting. Dept. of Statistics, Stanford University Technical Report.
13. M.H. Hansen, and B. Yu Model selection and the principle of minimum description length Journal of the American Statistical Association 96 2001 746 774 (Pubitemid 33695629)
14. T. Hastie, R. Tibishirani, and J. Friedman The Elements of Statistical Learning 2001 Springer-Verlag New York
15. C.M. Hurvich, J.S. Simonoff, and C.L. Tsai Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion Journal of the Royal Statistical Society, Series B: Statistical Methodology 60 1998 271 293
16. C.M. Hurvich, and C.L. Tsai Regression and time series model selection in small samples Biometrika 76 1989 297 307
17. L. Mason, J. Baxter, P. Bartkett, and M. Frean S.A. Solla, T.K. Leen, K.-R. Mller, Advance in Neural Information Processing Systems vol. 12 2000 Massachusette Institute of Technology 512 518
18. R. Meir, and G. Rtsch An introduction to boosting and leveraging S. Mendelson, A. Smola, Advanced Lectures on Machine Learning LNCS 2003 Springer New York 119 184
19. R. Shibata An optimal selection of regression variables Biometrika 68 1981 45 54
20. T.P. Speed, and B. Yu Model selection and prediction: normal regression Annals of the Institute of Statistical Mathematics 45 1993 35 54
21. N. Sugiura Further analysis of the data by Akaike's information criterion and the finite corrections Communications in Statistics: Theory and Methods 7 1978 13 26
22. C.A. Tsao, and Y.-C.I. Chang A stochastic approximation view of boosting Computational Statistics and Data Analysis 52 2007 325 334