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» Heteroscedastic Gaussian process regression
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DSMML
2004
Springer
15 years 2 months ago
Understanding Gaussian Process Regression Using the Equivalent Kernel
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 how to approximat...
Peter Sollich, Christopher K. I. Williams
ESANN
2008
14 years 11 months ago
Approximation of Gaussian process regression models after training
The evaluation of a standard Gaussian process regression model takes time linear in the number of training data points. In this paper, the models are approximated in the feature sp...
Thorsten Suttorp, Christian Igel
CVPR
2009
IEEE
16 years 4 months ago
Nonrigid Shape Recovery by Gaussian Process Regression
Most state-of-the-art nonrigid shape recovery methods usually use explicit deformable mesh models to regularize surface deformation and constrain the search space. These triangu...
Jianke Zhu, Michael R. Lyu, Steven C. H. Hoi
ICML
2008
IEEE
15 years 10 months ago
Sparse multiscale gaussian process regression
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of th...
Bernhard Schölkopf, Christian Walder, Kwang I...
75
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SDM
2009
SIAM
172views Data Mining» more  SDM 2009»
15 years 6 months ago
Travel-Time Prediction Using Gaussian Process Regression: A Trajectory-Based Approach.
This paper is concerned with the task of travel-time prediction for an arbitrary origin-destination pair on a map. Unlike most of the existing studies, which focus only on a parti...
Sei Kato, Tsuyoshi Idé