Sparse Boosting

Journal of Machine Learning Research

Volumn 7, Issue , 2006, Pages 1001-1024

  Bühlmann, Peter ^a   Yu, Bin ^b  

^a ETH ZURICH   (Switzerland)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Lasso; Minimum description length (MDL); Model selection; Nonnegative garrote; Regression

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; ERROR ANALYSIS; LINEAR SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING; REGRESSION ANALYSIS;

LASSO; MINIMUM DESCRIPTION LENGTH (MDL); MODEL SELECTION; NONNEGATIVE GARROTE;

EQUIVALENCE CLASSES;

EID: 33745125391     PISSN: 15337928     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (25)

1. H. Akaike. Statistical predictor identification. Ann. Inst. Statist. Math., 22:203, 1970.
2. L. Breiman. Better subset regression using the nonnegative garrote. Technometrics, 37:373-384, 1995.
3. L. Breiman. Bagging predictors. Machine Learning, 24:123-140, 1996.
4. L. Breiman. Arcing classifiers (with discussion). Ann. Statist., 26:801-849, 1998.
5. L. Breiman. Prediction games & arcing algorithms. Neural Computation, 11:1493-1517, 1999.
6. P. Bühlmann. Boosting for high-dimensional linear models. To appear in Ann. Statist., 34, 2006.
7. 2loss: regression and classification. J. Amer. Statist. Assoc., 98:324-339, 2003.
8. B. Efron, T. Hastie, L Johnstone, and R. Tibshirani. Least angle regression (with discussion). Ann. Statist., 32:407-451, 2004.
9. Y. Freund and R. E. Schapire. Experiments with a new boosting algorithm. In Machine Learning: Proc. Thirteenth Intern. Conf., pages 148-156. Morgan Kauffman, 1996.
10. J. H. Friedman. Greedy function approximation: a gradient boosting machine. Ann.Statist., 29: 1189-1232, 2001.
11. J. H. Friedman. Multivariate adaptive regression splines (with discussion). Ann.Statist., 19:1-141, 1991.
12. J. H. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting (with discussion). Ann. Statist., 28:337-407, 2000.
13. P. J. Green and B. W. Silverman. Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. Chapman and Hall, 1994.
14. M. Hansen and B. Yu. Model selection and minimum description length principle. J. Amer. Statist. Assoc., 96:746-774, 2001.
15. M. Hansen and B. Yu. Minimum Description Length Model Selection Criteria for Generalized Linear Models. IMS Lecture Notes - Monograph Series, Vol. 40, 2002.
16. M. Hansen and B. Yu. Bridging aie and bic: an mdl model selection criterion. In IEEE Information Theory Workshop on Detection, Imaging and Estimation; Santa Fe, 1999.
17. G. Lugosi and N. Vayatis. On the bayes-risk consistency of regularized boosting methods (with discussion). Ann. Statist., 32:30-55 (disc. pp. 85-134), 2004.
18. S. Mallat and Z. Zhang. Matching pursuits with time-frequency dictionaries. IEEE Trans. Signal Proc., 41:3397-3415, 1993.
19. N. Meinshausen. Lasso with relaxation. Technical report, 2005.
20. G. Rätsch, T. Onoda, and K.-R. Müller. Soft margins for adaboost. Machine Learning, 42:287-320., 2001.
21. G. Rätsch, A. Demiriz, and K. Bennett. Sparse regression ensembles in infinite and finite hypothesis spaces. Machine Learning, 48:193-221, 2002.
22. T. Speed and B. Yu. Model selection and prediction: normal regression. Ann. Inst. Statist. Math., 45:35-54, 1993.
23. R. Tibshirani. Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc., Ser. B, 58: 267-288, 1996.
24. J. W. Tukey. Exploratory data analysis. Addison-Wesley, 1977.
25. M. West, C. Blanchette, H. Dressman, E. Huang, S. Ishida, R. Spang, H. Zuzan, J. Olson, J. Marks, and J. Nevins. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc. Nat. Acad. Sci. (USA), 98:11462-11467, 2001.