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ICML
2005
IEEE
16 years 3 months ago
Hierarchic Bayesian models for kernel learning
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance w...
Mark Girolami, Simon Rogers
KES
2008
Springer
15 years 2 months ago
Super Resolution of Multispectral Images Using TV Image Models
Abstract. In this paper we propose a novel algorithm for the pansharpening of multispectral images based on the use of a Total Variation (TV) image prior. Within the Bayesian formu...
Miguel Vega, Javier Mateos, Rafael Molina, Aggelos...
VRST
2003
ACM
15 years 7 months ago
Incremental rendering of deformable trimmed NURBS surfaces
Trimmed NURBS surfaces are often used to model smooth and complex objects. Unfortunately, most existing hardware graphics accelerators cannot render them directly. Although there ...
Gary K. L. Cheung, Rynson W. H. Lau, Frederick W. ...
NIPS
2001
15 years 3 months ago
Covariance Kernels from Bayesian Generative Models
We propose the framework of mutual information kernels for learning covariance kernels, as used in Support Vector machines and Gaussian process classifiers, from unlabeled task da...
Matthias Seeger
CORR
2002
Springer
106views Education» more  CORR 2002»
15 years 2 months ago
On model selection and the disability of neural networks to decompose tasks
A neural network with fixed topology can be regarded as a parametrization of functions, which decides on the correlations between functional variations when parameters are adapted...
Marc Toussaint