79 search results - page 8 / 16 » Projected Subgradient Methods for Learning Sparse Gaussians |
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
This paper presents a novel Gaussian process (GP) approach to regression with inputdependent noise rates. We follow Goldberg et al.'s approach and model the noise variance us...
SADM
2010
13 years 2 months ago
2010
This paper is concerned with classifying high dimensional data into one of two categories. In various settings, such as when dealing with fMRI and microarray data, the number of v...
CVPR
2006
IEEE
14 years 9 months ago
2006
IEEE
We address the image denoising problem, where zeromean white and homogeneous Gaussian additive noise should be removed from a given image. The approach taken is based on sparse an...
JMLR
2002
13 years 7 months ago
2002
Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to support vector machines....
CORR
2010
Springer
13 years 7 months ago
2010
Springer
Given data drawn from a mixture of multivariate Gaussians, a basic problem is to accurately estimate the mixture parameters. We give an algorithm for this problem that has running ...