Distribution-Specific Agnostic Boosting

We consider the problem of boosting the accuracy of weak learning algorithms in the agnostic learning framework of Haussler (1992) and Kearns et al. (1992). Known algorithms for this problem (Ben-David et al., 2001; Gavinsky, 2002; Kalai et al. , 2008) follow the same strategy as boosting algorithms in the PAC model: the weak learner is executed on the same target function but over different distributions on the domain. Application of such boosting algorithms usually requires a distribution-independent weak agnostic learners. Here we demonstrate boosting algorithms for the agnostic learning framework that only modify the distribution on the labels of the points (or, equivalently, modify the target function). This allows boosting a distribution-specific weak agnostic learner to a strong agnostic learner with respect to the same distribution. Our algorithm achieves the same guarantees on the final error as the boosting algorithms of Kalai et al. (2008) but is substantially simpler and m...

• Type: Conference
• Year: 2010
• Where: ICS
• Authors: Vitaly Feldman
• Keywords: ICS 2010, Theoritical Computer Science