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    KDD
    2009
    ACM
    191views Data Mining» more  KDD 2009»
    Efficient methods for topic model inference on streaming document collections
    14 years 8 months ago
    Efficient methods for topic model inference on streaming document collections
    Download  www.cs.umass.edu
    Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of...
    Limin Yao, David M. Mimno, Andrew McCallum
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    UAI
    2008
    252views Artificial Intelligence» more  UAI 2008»
    Small Sample Inference for Generalization Error in Classification Using the CUD Bound
    13 years 9 months ago
    Small Sample Inference for Generalization Error in Classification Using the CUD Bound
    Download  www.stat.lsa.umich.edu
    Confidence measures for the generalization error are crucial when small training samples are used to construct classifiers. A common approach is to estimate the generalization err...
    Eric Laber, Susan Murphy
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    CVPR
    2004
    IEEE
    137views Computer Vision» more  CVPR 2004»
    Graphical Models for Graph Matching
    14 years 9 months ago
    Graphical Models for Graph Matching
    Download  www.tiberiocaetano.com
    This paper explores a formulation for attributed graph matching as an inference problem over a hidden Markov Random Field. We approximate the fully connected model with simpler mo...
    Dante Augusto Couto Barone, Terry Caelli, Tib&eacu...
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    AIPS
    2007
    156views Artificial Intelligence» more  AIPS 2007»
    Approximate Solution Techniques for Factored First-Order MDPs
    13 years 9 months ago
    Approximate Solution Techniques for Factored First-Order MDPs
    Download  www.cs.toronto.edu
    Most traditional approaches to probabilistic planning in relationally specified MDPs rely on grounding the problem w.r.t. specific domain instantiations, thereby incurring a com...
    Scott Sanner, Craig Boutilier
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    SIAMSC
    2011
    219views more  SIAMSC 2011»
    Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
    13 years 2 months ago
    Fast Algorithms for Bayesian Uncertainty Quantification in Large-Scale Linear Inverse Problems Based on Low-Rank Partial Hessian
    Download  lucaswilcox.com
    We consider the problem of estimating the uncertainty in large-scale linear statistical inverse problems with high-dimensional parameter spaces within the framework of Bayesian inf...
    H. P. Flath, Lucas C. Wilcox, Volkan Akcelik, Judi...
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