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NIPS
1994
15 years 8 months ago
Efficient Methods for Dealing with Missing Data in Supervised Learning
We present efficient algorithms for dealing with the problem of missing inputs (incomplete feature vectors) during training and recall. Our approach is based on the approximation ...
Volker Tresp, Ralph Neuneier, Subutai Ahmad
UAI
1996
15 years 8 months ago
Asymptotic Model Selection for Directed Networks with Hidden Variables
We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be ...
Dan Geiger, David Heckerman, Christopher Meek
CORR
2010
Springer
113views Education» more  CORR 2010»
15 years 6 months ago
Traffic Capacity of Large WDM Passive Optical Networks
As passive optical networks (PON) are increasingly deployed to provide high speed Internet access, it is important to understand their fundamental traffic capacity limits. The pape...
Nelson Antunes, Christine Fricker, Philippe Robert...
CORR
2008
Springer
118views Education» more  CORR 2008»
15 years 6 months ago
Descent methods for Nonnegative Matrix Factorization
In this paper, we present several descent methods that can be applied to nonnegative matrix factorization and we analyze a recently developped fast block coordinate method. We als...
Ngoc-Diep Ho, Paul Van Dooren, Vincent D. Blondel
COMPUTING
2006
119views more  COMPUTING 2006»
15 years 6 months ago
Sparse Grids, Adaptivity, and Symmetry
Sparse grid methods represent a powerful and efficient technique for the representation and approximation of functions and particularly the solutions of partial differential equat...
Harry Yserentant