Random Forests

@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}, journal = {Machine Learning}, keywords = {boosting, classification, regression}, number = {1}, pages = {5--32}, posted-at = {2005-06-08 16:23:24}, priority = {2}, publisher = {Kluwer Academic Publishers, Boston}, title = {Random Forests}, url = {http://citeseer.ist.psu.edu/breiman01random.html}, volume = {45}, year = {2001} }

TY - JOUR ID - citeulike:165116 L3 - citeulike-article-id:165116 TI - Random Forests JF - Machine Learning VL - 45 IS - 1 SP - 5 EP - 32 PB - Kluwer Academic Publishers, Boston 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 ... KW - boosting KW - classification KW - regression AU - Breiman, Leo PY - 2001/// UR - http://citeseer.ist.psu.edu/breiman01random.html ER -