Gaussian process

Known as: GP, Gaussian Processes, Gaussian stochastic process 
In probability theory and statistics, a Gaussian process is a statistical model where observations occur in a continuous domain, e.g. time or space… (More)
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Papers overview

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Highly Cited
2009
Highly Cited
2009
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have… (More)
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Highly Cited
2008
Highly Cited
2008
We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models… (More)
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Highly Cited
2007
Highly Cited
2007
In this paper we investigate multi-task learning in the context of Gaussian Processes (GP). We propose a model that learns a… (More)
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Highly Cited
2007
Highly Cited
2007
WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been… (More)
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Highly Cited
2005
Highly Cited
2005
We provide a new unifying view, including all existing prope r probabilistic sparse approximations for Gaussian process… (More)
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Highly Cited
2005
Highly Cited
2005
This paper introduces Gaussian Process Dynamical Models (GPDM) for nonlinear time series analysis. A GPDM comprises a low… (More)
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Highly Cited
2005
Highly Cited
2005
We present a new Gaussian process (GP) regression model whose covariance is parameterized by the the locations of M pseudo-input… (More)
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Highly Cited
2003
Highly Cited
2003
In this paper we introduce a new underlying probabilistic model for principal component analysis (PCA). Our formulation… (More)
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Highly Cited
2001
Highly Cited
2001
We present an extension to the Mixture of Experts (ME) model, where the individual experts are Gaussian Process (GP) regression… (More)
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Highly Cited
1995
Highly Cited
1995
The Bayesian analysis of neural networks is difficult because a simple prior over weights implies a complex prior distribution… (More)
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