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Book
778views
15 years 2 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
VIS
2005
IEEE
110views Visualization» more  VIS 2005»
14 years 5 months ago
Prefiltered Gaussian Reconstruction for High-Quality Rendering of Volumetric Data sampled on a Body-Centered Cubic Grid
In this paper a novel high-quality reconstruction scheme is presented. Although our method is mainly proposed to reconstruct volumetric data sampled on an optimal Body-Centered Cu...
Balázs Csébfalvi
GRAPHITE
2003
ACM
13 years 10 months ago
Smooth surface reconstruction from noisy range data
This paper shows that scattered range data can be smoothed at low cost by fitting a Radial Basis Function (RBF) to the data and convolving with a smoothing kernel (low pass filt...
Jonathan C. Carr, Richard K. Beatson, Bruce C. McC...
VLDB
1999
ACM
140views Database» more  VLDB 1999»
13 years 9 months ago
Combining Histograms and Parametric Curve Fitting for Feedback-Driven Query Result-size Estimation
This paper aims to improve the accuracy of query result-size estimations in query optimizers by leveraging the dynamic feedback obtained from observations on the executed query wo...
Arnd Christian König, Gerhard Weikum
CPHYSICS
2007
89views more  CPHYSICS 2007»
13 years 4 months ago
Numerical differentiation of experimental data: local versus global methods
In the context of the analysis of measured data, one is often faced with the task to differentiate data numerically. Typically, this occurs when measured data are concerned or dat...
Karsten Ahnert, Markus Abel