Random Forests Export

@article{citeulike:165116, 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 ...}, author = {Breiman, Leo}, citeulike-article-id = {165116}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.23.3999}, citeulike-linkout-1 = {http://www.springerlink.com/media/1F9VNMYRLQUG73W3DBE7/Contributions/U/0/P/0/U0P06167N6173512.pdf}, citeulike-linkout-2 = {http://dx.doi.org/10.1023/A:1010933404324}, doi = {10.1023/A:1010933404324}, journal = {Machine Learning}, keywords = {boosting, cart, classification, ensemble, machine-learning}, number = {1}, pages = {5--32}, posted-at = {2006-07-25 23:36:26}, priority = {2}, publisher = {Kluwer Academic Publishers, Boston}, title = {Random Forests}, url = {http://dx.doi.org/10.1023/A:1010933404324}, volume = {45}, year = {2001} }

TY - JOUR ID - citeulike:165116 L3 - citeulike-article-id:165116 N2 - 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 ... IS - 1 JF - Machine Learning EP - 32 TI - Random Forests PB - Kluwer Academic Publishers, Boston VL - 45 SP - 5 KW - boosting KW - cart KW - classification KW - ensemble KW - machine-learning AU - Breiman, Leo PY - 2001/// UR - http://dx.doi.org/10.1023/A:1010933404324 ER -