Additive logistic regression: a statistical view of boosting Export

@misc{citeulike:909961, abstract = {Boosting (Freund \& Schapire 1995) is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data, and then taking a weighted majority vote of the sequence of classifiers thus produced. For many classification algorithms, this simple strategy results in dramatic improvements in performance. We show that this seemingly mysterious phenomenon can be understood in terms of...}, author = {Friedman, J. and Hastie, T. and Tibshirani, R.}, citeulike-article-id = {909961}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.9525}, keywords = {adaboost, boosting, logistic, regression, statistical}, posted-at = {2008-03-10 20:12:54}, priority = {2}, title = {Additive logistic regression: a statistical view of boosting}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.9525}, year = {1998} }

TY - GEN ID - citeulike:909961 L3 - citeulike-article-id:909961 N2 - Boosting (Freund & Schapire 1995) is one of the most important recent developments in classification methodology. Boosting works by sequentially applying a classification algorithm to reweighted versions of the training data, and then taking a weighted majority vote of the sequence of classifiers thus produced. For many classification algorithms, this simple strategy results in dramatic improvements in performance. We show that this seemingly mysterious phenomenon can be understood in terms of... TI - Additive logistic regression: a statistical view of boosting KW - adaboost KW - boosting KW - logistic KW - regression KW - statistical AU - Friedman, J AU - Hastie, T AU - Tibshirani, R PY - 1998/// UR - http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.9525 ER -