18 search results - page 1 / 4 » Robust Regression with Twinned Gaussian Processes |
NIPS
2007
13 years 5 months ago
2007
We propose a Gaussian process (GP) framework for robust inference in which a GP prior on the mixing weights of a two-component noise model augments the standard process over laten...
DSMML
2004
Springer
13 years 9 months ago
2004
Springer
Learning curves for Gaussian process (GP) regression can be strongly affected by a mismatch between the ‘student’ model and the ‘teacher’ (true data generation process), e...
CVPR
2009
IEEE
14 years 11 months ago
2009
IEEE
Most state-of-the-art nonrigid shape recovery methods
usually use explicit deformable mesh models to regularize
surface deformation and constrain the search space. These
triangu...
CVPR
2010
IEEE
13 years 4 months ago
2010
IEEE
Kernel regression techniques such as Relevance Vector Machine (RVM) regression, Support Vector Regression and Gaussian processes are widely used for solving many computer vision p...
ICASSP
2010
IEEE
13 years 4 months ago
2010
IEEE
In this paper, we develop algorithms for robust linear regression by leveraging the connection between the problems of robust regression and sparse signal recovery. We explicitly ...