General bounds on Bayes errors for regression with Gaussian processes

Opper, Manfred and Vivarelli, Francesco (1999). General bounds on Bayes errors for regression with Gaussian processes. IN: Advances in Neural Information Processing Systems 1999. Neural information processing systems foundation.

Abstract

Based on a simple convexity lemma, we develop bounds for different types of Bayesian prediction errors for regression with Gaussian processes. The basic bounds are formulated for a fixed training set. Simpler expressions are obtained for sampling from an input distribution which equals the weight function of the covariance kernel, yielding asymptotically tight results. The results are compared with numerical experiments.

Divisions: Aston University (General)
Additional Information: Copyright of the Massachusetts Institute of Technology Press (MIT)
Event Title: 12th Annual Conference on Neural Information Processing Systems, NIPS 1998
Event Type: Other
Event Dates: 1998-11-30 - 1998-12-05
Uncontrolled Keywords: convexity,Bayesian prediction errors,regression,Gaussian processes,covariance kernel,asymptotically,Computer Networks and Communications,Information Systems,Signal Processing
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
http://mitpress ... type=2&tid=4746 (Publisher URL)
Published Date: 1999
Authors: Opper, Manfred
Vivarelli, Francesco

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