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ICANN
2011
Springer
12 years 8 months ago
Learning from Multiple Annotators with Gaussian Processes
Abstract. In many supervised learning tasks it can be costly or infeasible to obtain objective, reliable labels. We may, however, be able to obtain a large number of subjective, po...
Perry Groot, Adriana Birlutiu, Tom Heskes
BMVC
2002
13 years 7 months ago
Practical Generation of Video Textures using the Auto-Regressive Process
Recently, there have been several attempts at creating `video textures', that is, synthesising new (potentially infinitely long) video clips based on existing ones. One way t...
Neill W. Campbell, Colin J. Dalton, David P. Gibso...

Book
778views
15 years 3 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...
BMVC
2010
13 years 3 months ago
Local Gaussian Processes for Pose Recognition from Noisy Inputs
Gaussian processes have been widely used as a method for inferring the pose of articulated bodies directly from image data. While able to model complex non-linear functions, they ...
Martin Fergie, Aphrodite Galata
ICASSP
2011
IEEE
12 years 9 months ago
Gaussian Mixture Modeling of vowel durations for automated assessment of non-native speech
This paper investigates using Gaussian Mixture Model (GMM) based vowel duration features for automated assessment of non-native speech. Two different types of models were compared...
Xie Sun, Keelan Evanini