79 search results - page 4 / 16 » Projected Subgradient Methods for Learning Sparse Gaussians |
ICML
2008
IEEE
15 years 10 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
15 years 10 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
15 years 10 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
15 years 10 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
14 years 11 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...