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    JMLR
    2006
    113views more  JMLR 2006»
    Learning the Structure of Linear Latent Variable Models
    13 years 7 months ago
    Learning the Structure of Linear Latent Variable Models
    Download  www.hss.cmu.edu
    We describe anytime search procedures that (1) find disjoint subsets of recorded variables for which the members of each subset are d-separated by a single common unrecorded cause...
    Ricardo Silva, Richard Scheines, Clark Glymour, Pe...
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    ICML
    2010
    IEEE
    240views Machine Learning» more  ICML 2010»
    High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models
    13 years 8 months ago
    High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models
    Download  www.icml2010.org
    We develop a semi-supervised learning method that constrains the posterior distribution of latent variables under a generative model to satisfy a rich set of feature expectation c...
    Gregory Druck, Andrew McCallum
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    IFIP12
    2004
    104views Information Technology» more  IFIP12 2004»
    Introducing a Star Topology into Latent Class Models for Collaborative Filtering
    13 years 8 months ago
    Introducing a Star Topology into Latent Class Models for Collaborative Filtering
    Download  www.cs.bham.ac.uk
    Latent class models (LCM) represent the high dimensional data in a smaller dimensional space in terms of latent variables. They are able to automatically discover the patterns from...
    Gabriela Polcicova, Peter Tiño
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    IJON
    2008
    123views more  IJON 2008»
    Bayesian estimation of time delays between unevenly sampled signals
    13 years 7 months ago
    Bayesian estimation of time delays between unevenly sampled signals
    Download  www.cis.hut.fi
    A method for estimating time delays between signals that are irregularly sampled is presented. The approach is based on postulating a latent variable model from which the observed...
    Markus Harva, Somak Raychaudhury
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    EMNLP
    2007
    144views Natural Language Processing» more  EMNLP 2007»
    A Topic Model for Word Sense Disambiguation
    13 years 8 months ago
    A Topic Model for Word Sense Disambiguation
    Download  wordnet.cs.princeton.edu
    We develop latent Dirichlet allocation with WORDNET (LDAWN), an unsupervised probabilistic topic model that includes word sense as a hidden variable. We develop a probabilistic po...
    Jordan L. Boyd-Graber, David M. Blei, Xiaojin Zhu
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