Learning Ensembles of Structured Prediction Rules

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri

Abstract: We present a series of algorithms with theoretical guarantees for learning accurate ensembles of several structured prediction rules for which no prior knowledge is assumed. This includes a number of randomized and deterministic algorithms devised by converting on-line learning algorithms to batch ones, and a boostingstyle algorithm applicable in the context of structured prediction with a large number of labels. We also report the results of extensive experiments with these algorithms.