372 search results - page 1 / 75 » Covariance Kernels from Bayesian Generative Models |
NIPS
2001
13 years 6 months ago
2001
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...
CDC
2009
IEEE
13 years 9 months ago
2009
IEEE
This paper adresses the variance quantification problem for system identification based on the prediction error framework. The role of input and model class selection for the auto-...
SDM
2004
SIAM
13 years 6 months ago
2004
SIAM
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
NIPS
2004
13 years 6 months ago
2004
We present a novel method for learning with Gaussian process regression in a hierarchical Bayesian framework. In a first step, kernel matrices on a fixed set of input points are l...
NIPS
2007
13 years 6 months ago
2007
We show how to use unlabeled data and a deep belief net (DBN) to learn a good covariance kernel for a Gaussian process. We first learn a deep generative model of the unlabeled da...