DOI: 10.1023/a:1010933404324. Random Forests

Random Forests

10.1023/a:1010933404324

Crossref journal-article

Springer Science and Business Media LLC

Machine Learning (297)

Bibliography

Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5â32.

Authors 1

Leo Breiman (first)

References 17 Referenced 95,557

Amit, Y. & Geman, D. (1997). Shape quantization and recognition with randomized trees. Neural Computation, 9, 1545–1588. (10.1162/neco.1997.9.7.1545) / Neural Computation by Y. Amit (1997)
Amit, Y., Blanchard, G., & Wilder, K. (1999). Multiple randomized classifiers: MRCL Technical Report, Department of Statistics, University of Chicago.
Bauer, E. & Kohavi, R. (1999). An empirical comparison of voting classification algorithms. Machine Learning, 36(1/2), 105–139. (10.1023/A:1007515423169) / Machine Learning by E. Bauer (1999)
Breiman, L. (1996a). Bagging predictors. Machine Learning 26(2), 123–140. (10.1023/A:1018054314350) / Machine Learning by L. Breiman (1996)
Breiman, L. (1996b). Out-of-bag estimation, ftp.stat.berkeley.edu/pub/users/breiman/OOBestimation.ps
Breiman, L. (1998a). Arcing classifiers (discussion paper). Annals of Statistics, 26, 801–824. (10.1214/aos/1024691079) / Annals of Statistics by L. Breiman (1998)
Breiman. L. (1998b). Randomizing outputs to increase prediction accuracy. Technical Report 518, May 1, 1998, Statistics Department, UCB (in press, Machine Learning).
Breiman, L. 1999. Using adaptive bagging to debias regressions. Technical Report 547, Statistics Dept. UCB.
Breiman, L. 2000. Some infinity theory for predictor ensembles. Technical Report 579, Statistics Dept. UCB.
Dietterich, T. (1998). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization, Machine Learning, 1–22.
Freund, Y. & Schapire, R. (1996). Experiments with a new boosting algorithm, Machine Learning: Proceedings of the Thirteenth International Conference, 148–156.
Grove, A. & Schuurmans, D. (1998). Boosting in the limit: Maximizing the margin of learned ensembles. In Proceedings of the Fifteenth National Conference on Artificial Intelligence (AAAI-98).
Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Trans. on Pattern Analysis and Machine Intelligence, 20(8), 832–844. (10.1109/34.709601) / IEEE Trans. on Pattern Analysis and Machine Intelligence by T. K. Ho (1998)
Kleinberg, E. (2000). On the algorithmic implementation of stochastic discrimination. IEEE Trans. on Pattern Analysis and Machine Intelligence, 22(5), 473–490. (10.1109/34.857004) / IEEE Trans. on Pattern Analysis and Machine Intelligence by E. Kleinberg (2000)
Schapire, R., Freund, Y., Bartlett, P., & Lee,W. (1998). Boosting the margin:Anewexplanation for the effectiveness of voting methods. Annals of Statistics, 26(5), 1651–1686. / Annals of Statistics by R. Schapire (1998)
Tibshirani, R. (1996). Bias, variance, and prediction error for classification rules. Technical Report, Statistics Department, University of Toronto.
Wolpert, D. H. & Macready, W. G. (1997). An efficient method to estimate Bagging's generalization error (in press, Machine Learning).

Dates

Type	When
Created	22 years, 7 months ago (Dec. 22, 2002, 6:10 p.m.)
Deposited	1 month, 1 week ago (July 10, 2025, 7:46 a.m.)
Indexed	38 minutes ago (Aug. 21, 2025, 6:26 a.m.)
Issued	23 years, 10 months ago (Oct. 1, 2001)
Published	23 years, 10 months ago (Oct. 1, 2001)
Published Print	23 years, 10 months ago (Oct. 1, 2001)

Funders 0

None

BibTeX

@article{Breiman_2001, title={Random Forests}, volume={45}, ISSN={1573-0565}, url={http://dx.doi.org/10.1023/a:1010933404324}, DOI={10.1023/a:1010933404324}, number={1}, journal={Machine Learning}, publisher={Springer Science and Business Media LLC}, author={Breiman, Leo}, year={2001}, month=oct, pages={5–32} }

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