Some Theoretical Aspects of Boosting in the Presence of Noisy Data

This is a survey of some theoretical results on boosting obtained from an analogous treatment of some regression and classi cation boosting algorithms. Some related papers include J99 and J00a,b,c,d , which is a set of mutually overlapping papers concerning the assumption of weak hypotheses, behavior of generalization error in the large time limit and during the process of boosting, comparison to the optimal Bayes error in noisy situations, over tting, and regularization.

• Type: Conference
• Year: 2001
• Where: ICML
• Authors: Wenxin Jiang
• Keywords: Cation Boosting Algorithms, Generalization Error, ICML 2001, Large Time Limit, Machine Learning