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» Hierarchical Gaussian Process Regression
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ICML
2006
IEEE
14 years 6 months ago
Collaborative ordinal regression
Ordinal regression has become an effective way of learning user preferences, but most of research only focuses on single regression problem. In this paper we introduce collaborati...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...
BMCBI
2007
194views more  BMCBI 2007»
13 years 5 months ago
Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
Xin Zhao, Leo Wang-Kit Cheung
ICML
2005
IEEE
14 years 6 months ago
Heteroscedastic Gaussian process regression
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 variance l...
Alexander J. Smola, Quoc V. Le, Stéphane Ca...
ICASSP
2010
IEEE
13 years 5 months ago
Fast likelihood computation using hierarchical Gaussian shortlists
We investigate the use of hierarchical Gaussian shortlists to speed up Gaussian likelihood computation. This approach is a combination of hierarchical Gaussian selection and stand...
Xin Lei, Arindam Mandal, Jing Zheng
NIPS
2004
13 years 6 months ago
Using the Equivalent Kernel to Understand Gaussian Process Regression
The equivalent kernel [1] is a way of understanding how Gaussian process regression works for large sample sizes based on a continuum limit. In this paper we show (1) how to appro...
Peter Sollich, Christopher K. I. Williams