1658 search results - page 120 / 332 » Learning Mixtures of Gaussians |
170
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NIPS
2007
15 years 7 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...
194
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ICANN
2011
Springer
14 years 10 months ago
2011
Springer
This paper is concerned with estimation of learning curves for Gaussian process regression with multidimensional numerical integration. We propose an approach where the recursion e...
165
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ICASSP
2010
IEEE
15 years 5 months ago
2010
IEEE
This paper addresses the problem of state estimation in the case where the prior distribution of the states is not perfectly known but instead is parameterized by some unknown par...
185
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JMLR
2010
15 years 1 months ago
2010
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library ...
263
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Book
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...