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JAIR
2010
131views more  JAIR 2010»
13 years 5 months ago
Automatic Induction of Bellman-Error Features for Probabilistic Planning
Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provide...
Jia-Hong Wu, Robert Givan
ICML
2008
IEEE
14 years 8 months ago
On the quantitative analysis of deep belief networks
Deep Belief Networks (DBN's) are generative models that contain many layers of hidden variables. Efficient greedy algorithms for learning and approximate inference have allow...
Ruslan Salakhutdinov, Iain Murray
ANNPR
2006
Springer
13 years 11 months ago
Support Vector Regression Using Mahalanobis Kernels
Abstract. In our previous work we have shown that Mahalanobis kernels are useful for support vector classifiers both from generalization ability and model selection speed. In this ...
Yuya Kamada, Shigeo Abe
IDA
2009
Springer
14 years 1 months ago
Bayesian Non-negative Matrix Factorization
Abstract. We present a Bayesian treatment of non-negative matrix factorization (NMF), based on a normal likelihood and exponential priors, and derive an efficient Gibbs sampler to ...
Mikkel N. Schmidt, Ole Winther, Lars Kai Hansen
ICC
2007
IEEE
159views Communications» more  ICC 2007»
14 years 1 months ago
Limited Feedback Precoding in Realistic MIMO Channel Conditions
— MIMO system design for real applications requires that factors such as channel estimation errors, feedback quantisation and realistic channel responses be taken into account. L...
Geoffrey W. K. Colman, Tricia J. Willink