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    NN
    2010
    Springer
    189views Neural Networks» more  NN 2010»
    Sparse kernel learning with LASSO and Bayesian inference algorithm
    13 years 2 months ago
    Sparse kernel learning with LASSO and Bayesian inference algorithm
    Download  www.eis.mdx.ac.uk
    Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers (Gao et al., 2008) and (Wang et al., 2007). This paper is co...
    Junbin Gao, Paul W. Kwan, Daming Shi
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    CVPR
    2003
    IEEE
    194views Computer Vision» more  CVPR 2003»
    Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference
    14 years 9 months ago
    Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference
    Download  research.microsoft.com
    12 In this paper we study the problem of shape analysis and its application in locating facial feature points on frontal faces. We propose a Bayesian inference solution based on ta...
    Yi Zhou, Lie Gu, HongJiang Zhang
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    NIPS
    2003
    146views Information Technology» more  NIPS 2003»
    On the Concentration of Expectation and Approximate Inference in Layered Networks
    13 years 8 months ago
    On the Concentration of Expectation and Approximate Inference in Layered Networks
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    We present an analysis of concentration-of-expectation phenomena in layered Bayesian networks that use generalized linear models as the local conditional probabilities. This frame...
    XuanLong Nguyen, Michael I. Jordan
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    AAAI
    2008
    123views Intelligent Agents» more  AAAI 2008»
    Latent Tree Models and Approximate Inference in Bayesian Networks
    13 years 9 months ago
    Latent Tree Models and Approximate Inference in Bayesian Networks
    Download  www.aaai.org
    We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
    Yi Wang, Nevin Lianwen Zhang, Tao Chen
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    UAI
    1996
    135views Artificial Intelligence» more  UAI 1996»
    Asymptotic Model Selection for Directed Networks with Hidden Variables
    13 years 8 months ago
    Asymptotic Model Selection for Directed Networks with Hidden Variables
    Download  research.microsoft.com
    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
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