On-line Learning Approach to Ensemble Methods for Structured Prediction
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri · 2014
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. We also report the results of experiments with these algorithms on various structured prediction tasks.