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NIPS
2008
11 years 2 months ago
Analyzing human feature learning as nonparametric Bayesian inference
Almost all successful machine learning algorithms and cognitive models require powerful representations capturing the features that are relevant to a particular problem. We draw o...
Joseph Austerweil, Thomas L. Griffiths
ICASSP
2011
IEEE
10 years 5 months ago
Nonparametric Bayesian feature selection for multi-task learning
We present a nonparametric Bayesian model for multi-task learning, with a focus on feature selection in binary classification. The model jointly identifies groups of similar tas...
Hui Li, Xuejun Liao, Lawrence Carin
TSP
2010
10 years 8 months ago
Compressive Sensing on Manifolds Using a Nonparametric Mixture of Factor Analyzers: Algorithm and Performance Bounds
Nonparametric Bayesian methods are employed to constitute a mixture of low-rank Gaussians, for data x RN that are of high dimension N but are constrained to reside in a low-dimen...
Minhua Chen, Jorge Silva, John William Paisley, Ch...
NECO
2008
134views more  NECO 2008»
11 years 1 months ago
Latent Features in Similarity Judgments: A Nonparametric Bayesian Approach
One of the central problems in cognitive science is determining the mental representations that underlie human inferences. Solutions to this problem often rely on the analysis of ...
Daniel J. Navarro, Thomas L. Griffiths
ICML
2006
IEEE
12 years 2 months ago
A choice model with infinitely many latent features
Elimination by aspects (EBA) is a probabilistic choice model describing how humans decide between several options. The options from which the choice is made are characterized by b...
Carl Edward Rasmussen, Dilan Görür, Fran...
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