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» Using Gaussian Processes to Optimize Expensive Functions
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PKDD
2010
Springer
184views Data Mining» more  PKDD 2010»
13 years 6 months ago
Shift-Invariant Grouped Multi-task Learning for Gaussian Processes
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...
Yuyang Wang, Roni Khardon, Pavlos Protopapas
GECCO
2004
Springer
143views Optimization» more  GECCO 2004»
14 years 28 days ago
Efficient Clustering-Based Genetic Algorithms in Chemical Kinetic Modelling
Two efficient clustering-based genetic algorithms are developed for the optimisation of reaction rate parameters in chemical kinetic modelling. The genetic algorithms employed are ...
Lionel Elliott, Derek B. Ingham, Adrian G. Kyne, N...
ICONIP
2007
13 years 9 months ago
Discovery of Linear Non-Gaussian Acyclic Models in the Presence of Latent Classes
Abstract. An effective way to examine causality is to conduct an experiment with random assignment. However, in many cases it is impossible or too expensive to perform controlled ...
Shohei Shimizu, Aapo Hyvärinen
RSS
2007
159views Robotics» more  RSS 2007»
13 years 9 months ago
Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders
— In probabilistic mobile robotics, the development of measurement models plays a crucial role as it directly influences the efficiency and the robustness of the robot’s perf...
Christian Plagemann, Kristian Kersting, Patrick Pf...
ICIP
2006
IEEE
14 years 9 months ago
Using Non-Parametric Kernel to Segment and Smooth Images Simultaneously
Piecewise constant and piecewise smooth Mumford-Shah (MS) models have been widely studied and used for image segmentation. More complicated than piecewise constant MS, global Gaus...
Weihong Guo, Yunmei Chen