PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Heteroscedastic gaussian process regression
quoc le, Alex Smola and Stéphane Canu
In: ICML 2005, July 2005, Bonn.

Abstract

This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate vari- ance local ly unlike standard Gaussian Pro- cess regression or SVMs. This means that our estimator adapts to the local noise. The problem is cast in the setting of maximum a posteriori estimation in exponential families. Unlike previous work, we obtain a convex op- timization problem which can be solved via Newton’s method.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2034
Deposited By:Alex Smola
Deposited On:16 January 2006