A conversation with Leo Breiman. (English) Zbl 1059.01542


01A70 Biographies, obituaries, personalia, bibliographies



Biographic References:

Breiman, L.


Full Text: DOI


[1] Breiman, L. (1957). The individual ergodic theorem of information theory. Ann. Math. Statist. 28 809-811. [Correction · Zbl 0078.31801
[2] . 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. · Zbl 0109.36803
[4] Breiman, L. (1963). The Poisson tendency in traffic distribution. Ann. Math. Statist. 34 308-311. · Zbl 0117.13701
[5] Breiman, L. (1968). Probability Theory. Addison-Wesley, Reading, MA. [Republished (1991) in Classics of Mathematics. SIAM, Philadelphia.] · Zbl 0174.48801
[6] 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.) JSTOR: · Zbl 0742.62037
[7] Breiman, L. (1992). Submodel selection and evaluation in regression-The X-fixed case and little bootstrap. J. Amer. Statist. Assoc. 87 734-751. JSTOR: · Zbl 0850.62518
[8] Breiman, L. (1994). The 1990 Census adjustment-Undercount or bad data? (with discussion). Statist. Sci. 9 458-475. Breiman, L. (1996a). Bagging predictors. Machine Learning 26 123-140
[9] Breiman, L. (1998). Arcing classifiers (with discussion). Ann. Statist. 26 801-849. · Zbl 0934.62064
[10] 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.) Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, JSTOR: · Zbl 0594.62044
[11] C. J. (1984). Classification and Regression Trees. Wadsworth, Belmont, CA. (Since 1993 this book has been published by Chapman and Hall, New York.) · Zbl 0541.62042
[12] Freund, Y. and Schapire, R. (1996). Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International Conference 148-156.
[13] 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. · Zbl 0880.68103
[14] Ji, C. and Ma, S. (1997). Combinations of weak classifiers. IEEE Trans. Neural Networks (Special Issue) 8 32-42.
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