Is regularization unnecessary for boosting
Wenxin Jiang · 2001
this paper we present examples where `boosting forever ' leads to suboptimal predictions; while some regularization method, on the other hand, can achieve asymptotic optimality, at least in theory. We conjecture that this can be true in more general situations, and for some other regularization methods as well. Therefore the emerging literature on regularized variants of boosting is not unnecessary, but should be encouraged instead. The results of this paper are obtained from an analogy between some boosting algorithms that are used in regression and classification.