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» Perspectives on Sparse Bayesian Learning
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ICML
2005
IEEE
16 years 2 months ago
Healing the relevance vector machine through augmentation
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
Carl Edward Rasmussen, Joaquin Quiñonero Ca...
JMLR
2010
163views more  JMLR 2010»
14 years 8 months ago
Dense Message Passing for Sparse Principal Component Analysis
We describe a novel inference algorithm for sparse Bayesian PCA with a zero-norm prior on the model parameters. Bayesian inference is very challenging in probabilistic models of t...
Kevin Sharp, Magnus Rattray
PERCOM
2007
ACM
16 years 23 days ago
Structural Learning of Activities from Sparse Datasets
Abstract. A major challenge in pervasive computing is to learn activity patterns, such as bathing and cleaning from sensor data. Typical sensor deployments generate sparse datasets...
Fahd Albinali, Nigel Davies, Adrian Friday
COLT
2004
Springer
15 years 4 months ago
Suboptimal Behavior of Bayes and MDL in Classification Under Misspecification
We show that forms of Bayesian and MDL inference that are often applied to classification problems can be inconsistent. This means that there exists a learning problem such that fo...
Peter Grünwald, John Langford
ML
2006
ACM
142views Machine Learning» more  ML 2006»
15 years 1 months ago
The max-min hill-climbing Bayesian network structure learning algorithm
We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
Ioannis Tsamardinos, Laura E. Brown, Constantin F....