journal contribution
posted on 1999-01-01, 00:00 authored by Geoffrey J. Gordon We present a unified framework for reasoning about
worst-case regret bounds for learning algorithms.
This framework is based on the theory of duality
of convex functions. It brings together results from
computational learning theory and Bayesian statistics,
allowing us to derive new proofs of known
theorems, new theorems about known algorithms,
and new algorithms.
History
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© ACM, 1999. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the twelfth annual conference on Computational learning theory {1-58113-167-4 (1999)
http://doi.acm.org/10.1145/307400.307410Date
1999-01-01