%0 Journal Article %1 ridgeway_state_1999 %A Ridgeway, Greg %D 1999 %J Computing Science and Statistics %K Boosting %P 172--181 %T The state of boosting %V 31 %X In many problem domains, combining the predictions of several models often results in a model with improved predictive performance. Boosting is one such method that has shown great promise. On the applied side, empirical studies have shown that combining models using boosting methods produces more accurate classification and regression models. These methods are extendible to the exponential family as well as proportional hazards regression models. This article shows that boosting, which is still new to statistics, is widely applicable. I will introduce boosting, discuss the current state of boosting, and show how these methods connect to more standard statistical practice.

@article{ridgeway_state_1999, abstract = {In many problem domains, combining the predictions of several models often results in a model with improved predictive performance. Boosting is one such method that has shown great promise. On the applied side, empirical studies have shown that combining models using boosting methods produces more accurate classification and regression models. These methods are extendible to the exponential family as well as proportional hazards regression models. This article shows that boosting, which is still new to statistics, is widely applicable. I will introduce boosting, discuss the current state of boosting, and show how these methods connect to more standard statistical practice.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Ridgeway, Greg}, biburl = {https://www.bibsonomy.org/bibtex/28770eda8c8572b04f59cd1d1c1cffe56/yourwelcome}, interhash = {e15ef994b4cb310ba186a79851731229}, intrahash = {8770eda8c8572b04f59cd1d1c1cffe56}, journal = {Computing Science and Statistics}, keywords = {Boosting}, pages = {172--181}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {The state of boosting}, volume = 31, year = 1999 }