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    ICML
    2008
    IEEE
    118views Machine Learning» more  ICML 2008»
    The dynamic hierarchical Dirichlet process
    14 years 9 months ago
    The dynamic hierarchical Dirichlet process
    Download  people.ee.duke.edu
    The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are r...
    Lu Ren, David B. Dunson, Lawrence Carin
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    JMLR
    2006
    105views more  JMLR 2006»
    Linear State-Space Models for Blind Source Separation
    13 years 8 months ago
    Linear State-Space Models for Blind Source Separation
    Download  jmlr.csail.mit.edu
    We apply a type of generative modelling to the problem of blind source separation in which prior knowledge about the latent source signals, such as time-varying auto-correlation a...
    Rasmus Kongsgaard Olsson, Lars Kai Hansen
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    GI
    2007
    Springer
    206views Theoretical Computer Science» more  GI 2007»
    Background Modeling Using Adaptive Cluster Density Estimation for Automatic Human Detection
    14 years 2 months ago
    Background Modeling Using Adaptive Cluster Density Estimation for Automatic Human Detection
    Download  www.dcs.lancs.ac.uk
    : Detection is an inherent part of every advanced automatic tracking system. In this work we focus on automatic detection of humans by enhanced background subtraction. Background s...
    Harish Bhaskar, Lyudmila Mihaylova, Simon Maskell
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    ICML
    2004
    IEEE
    128views Machine Learning» more  ICML 2004»
    Learning random walk models for inducing word dependency distributions
    14 years 9 months ago
    Learning random walk models for inducing word dependency distributions
    Download  nlp.stanford.edu
    Many NLP tasks rely on accurately estimating word dependency probabilities P(w1|w2), where the words w1 and w2 have a particular relationship (such as verb-object). Because of the...
    Kristina Toutanova, Christopher D. Manning, Andrew...
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    ICML
    2005
    IEEE
    169views Machine Learning» more  ICML 2005»
    Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
    14 years 9 months ago
    Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
    Download  www.cheng1.net
    We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
    Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...
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