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» Modelling Smooth Paths Using Gaussian Processes
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2007
151views Robotics» more  RSS 2007»
13 years 10 months ago
Adaptive Non-Stationary Kernel Regression for Terrain Modeling
— Three-dimensional digital terrain models are of fundamental importance in many areas such as the geo-sciences and outdoor robotics. Accurate modeling requires the ability to de...
Tobias Lang, Christian Plagemann, Wolfram Burgard
DAGM
2010
Springer
13 years 9 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen
SDM
2009
SIAM
172views Data Mining» more  SDM 2009»
14 years 5 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
ICCD
2007
IEEE
120views Hardware» more  ICCD 2007»
14 years 5 months ago
Statistical timing analysis using Kernel smoothing
We have developed a new statistical timing analysis approach that does not impose any assumptions on the nature of manufacturing variability and takes into account an arbitrary mo...
Jennifer L. Wong, Azadeh Davoodi, Vishal Khandelwa...
WSC
2008
13 years 11 months ago
Smooth flexible models of nonhomogeneous poisson processes using one or more process realizations
We develop and evaluate a semiparametric method to estimate the mean-value function of a nonhomogeneous Poisson process (NHPP) using one or more process realizations observed over...
Michael E. Kuhl, Shalaka C. Deo, James R. Wilson