| Author | Title | Year | Journal/Proceedings | Reftype | DOI/URL |
|---|---|---|---|---|---|
| Breiman, L. | Random Forests | 2001 | Machine Learning Vol. 45(1), pp. 5-32 |
article | DOI URL |
| Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to | |||||
BibTeX:
@article{breiman2001random,
author = {Breiman, Leo},
title = {Random Forests},
journal = {Machine Learning},
publisher = {Kluwer Academic Publishers},
year = {2001},
volume = {45},
number = {1},
pages = {5-32},
url = {http://dx.doi.org/10.1023/A%3A1010933404324},
doi = {http://dx.doi.org/10.1023/A:1010933404324}
}
|
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