@inproceedings{5b1c6441cf144ac8bed9350bfcbc3ea7,
title = "Data-Dependent Bounds for Bayesian Mixture Methods",
abstract = "We consider Bayesian mixture approaches, where a predictor is constructed by forming a weighted average of hypotheses from some space of functions. While such procedures are known to lead to optimal predictors in several cases, where sufficiently accurate prior information is available, it has not been clear how they perform when some of the prior assumptions are violated. In this paper we establish data-dependent bounds for such procedures, extending previous randomized approaches such as the Gibbs algorithm to a fully Bayesian setting. The finite-sample guarantees established in this work enable the utilization of Bayesian mixture approaches in agnostic settings, where the usual assumptions of the Bayesian paradigm fail to hold. Moreover, the bounds derived can be directly applied to non-Bayesian mixture approaches such as Bagging and Boosting.",
author = "Ron Meir and Tong Zhang",
note = "Publisher Copyright: {\textcopyright} NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems. All rights reserved.; 15th International Conference on Neural Information Processing Systems, NIPS 2002 ; Conference date: 09-12-2002 Through 14-12-2002",
year = "2002",
language = "English (US)",
series = "NIPS 2002: Proceedings of the 15th International Conference on Neural Information Processing Systems",
publisher = "MIT Press Journals",
pages = "319--326",
editor = "Suzanna Becker and Sebastian Thrun and Klaus Obermayer",
booktitle = "NIPS 2002",
address = "United States",
}