A Conversation with Leo Breiman

Statistical Science

Volumn 16, Issue 2, 2001, Pages 184-198

  Olshen, Richard ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0041692391     PISSN: 08834237     EISSN: None     Source Type: Journal    
DOI: 10.1214/ss/1009213290     Document Type: Article

References (17)

1. BREIMAN, L. (1957). The individual ergodic theorem of information theory. Ann. Math. Statist. 28 809-811. [Correction (1960). Ann. Math. Statist. 31 809-810.]
2. BREIMAN, L. (1957). The individual ergodic theorem of information theory. Ann. Math. Statist. 28 809-811. [Correction (1960). Ann. Math. Statist. 31 809-810.]
3. BREIMAN, L. (1960). Optimal gambling systems for favorable games. Proc. Fourth Berkeley Symp. Math. Statist. Probab. 1 60-77. Univ. California Press.
4. BREIMAN, L. (1963). The Poisson tendency in traffic distribution. Ann. Math. Statist. 34 308-311.
5. BREIMAN, L. (1968). Probability Theory. Addison-Wesley, Reading, MA. [Republished (1991) in Classics of Mathematics. SIAM, Philadelphia. ]
6. BREIMAN, L. (1968). Probability Theory. Addison-Wesley, Reading, MA. [Republished (1991) in Classics of Mathematics. SIAM, Philadelphia. ]
7. BREIMAN, L. (1991). The II-method for estimating multivariate functions from noisy data (with discussion). Technometrics. 33 125-160. (Awarded the Youden Prize as the best expository paper of the year in Technometrics.)
8. BREIMAN, L. (1992). Submodel selection and evaluation in regression - The X-fixed case and little bootstrap. J. Amer. Statist. Assoc. 87 734-751.
9. BREIMAN, L. (1994). The 1990 Census adjustment - Undercount or bad data? (with discussion). Statist. Sci. 9 458-475.
10. BREIMAN, L. (1996a). Bagging predictors. Machine Learning 26 123-140
11. BREIMAN, L. (1996b). The heuristics of instability in model selection. Ann. Statist. 24 2350-2383.
12. BREIMAN, L. (1998). Arcing classifiers (with discussion). Ann. Statist. 26 801-849.
13. BREIMAN, L. and FRIEDMAN, J.H. (1985). Estimating optimal transformations in multiple regression and correction (with discussion). J. Amer. Statist. Assoc. 80 580-619. (Theory and Methods Paper of the Year.)
14. BREIMAN, L., FRIEDMAN, J. H., OLSHEN, R. A., and STONE, C. J. (1984). Classification and Regression Trees. Wadsworth, Belmont, CA. (Since 1993 this book has been published by Chapman and Hall, New York.)
15. FREUND, Y. and SCHAPIRE, R. (1996). Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International Conference 148-156.
16. FREUND, Y. and SCHAPIRE, R. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. System Sci. 55 119-139.
17. JI, C. and MA, S. (1997). Combinations of weak classifiers. IEEE Trans. Neural Networks (Special Issue) 8 32-42.