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ICDM
2009
IEEE
155views Data Mining» more  ICDM 2009»
14 years 3 months ago
Stacked Gaussian Process Learning
—Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utili...
Marion Neumann, Kristian Kersting, Zhao Xu, Daniel...
CORR
2006
Springer
86views Education» more  CORR 2006»
13 years 8 months ago
Optimal Distortion-Power Tradeoffs in Sensor Networks: Gauss-Markov Random Processes
We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements ...
Nan Liu, Sennur Ulukus
ICASSP
2011
IEEE
13 years 9 days ago
MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations
The paper proposes a modification of the standard maximum a posteriori (MAP) method for the estimation of the parameters of a Gaussian process for cases where the process is supe...
Alexander Krueger, Reinhold Haeb-Umbach
ESANN
2006
13 years 10 months ago
Stochastic Processes for Canonical Correlation Analysis
We consider two stochastic process methods for performing canonical correlation analysis (CCA). The first uses a Gaussian Process formulation of regression in which we use the cur...
Colin Fyfe, Gayle Leen
ICML
2009
IEEE
14 years 9 months ago
Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities
The inhomogeneous Poisson process is a point process that has varying intensity across its domain (usually time or space). For nonparametric Bayesian modeling, the Gaussian proces...
Ryan Prescott Adams, Iain Murray, David J. C. MacK...