79 search results - page 4 / 16 » Projected Subgradient Methods for Learning Sparse Gaussians |
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of th...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
We consider the problem of fitting a large-scale covariance matrix to multivariate Gaussian data in such a way that the inverse is sparse, thus providing model selection. Beginnin...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
In the paper we propose a new type of regularization procedure for training sparse Bayesian methods for classification. Transforming Hessian matrix of log-likelihood function to d...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Point processes are difficult to analyze because they provide only a sparse and noisy observation of the intensity function driving the process. Gaussian Processes offer an attrac...
NIPS
2008
13 years 9 months ago
2008
We consider the problem of multiple kernel learning (MKL), which can be formulated as a convex-concave problem. In the past, two efficient methods, i.e., Semi-Infinite Linear Prog...