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    UAI
    2001
    140views Artificial Intelligence» more  UAI 2001»
    Learning the Dimensionality of Hidden Variables
    14 years 1 months ago
    Learning the Dimensionality of Hidden Variables
    Download  pluto.huji.ac.il
    A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Dete...
    Gal Elidan, Nir Friedman
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    NIPS
    2007
    158views Information Technology» more  NIPS 2007»
    Agreement-Based Learning
    14 years 1 months ago
    Agreement-Based Learning
    Download  books.nips.cc
    The learning of probabilistic models with many hidden variables and nondecomposable dependencies is an important and challenging problem. In contrast to traditional approaches bas...
    Percy Liang, Dan Klein, Michael I. Jordan
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    NIPS
    2007
    147views Information Technology» more  NIPS 2007»
    Convex Relaxations of Latent Variable Training
    14 years 1 months ago
    Convex Relaxations of Latent Variable Training
    Download  books.nips.cc
    We investigate a new, convex relaxation of an expectation-maximization (EM) variant that approximates a standard objective while eliminating local minima. First, a cautionary resu...
    Yuhong Guo, Dale Schuurmans
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    PKDD
    2009
    Springer
    146views Data Mining» more  PKDD 2009»
    Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
    14 years 5 months ago
    Parallel Subspace Sampling for Particle Filtering in Dynamic Bayesian Networks
    Download  cs.unm.edu
    Monitoring the variables of real world dynamic systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding prob...
    Eva Besada-Portas, Sergey M. Plis, Jesús Ma...
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    ICML
    2004
    IEEE
    133views Machine Learning» more  ICML 2004»
    Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
    14 years 5 months ago
    Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs
    Download  www.machinelearning.org
    We introduce a new perceptron-based discriminative learning algorithm for labeling structured data such as sequences, trees, and graphs. Since it is fully kernelized and uses poin...
    Hisashi Kashima, Yuta Tsuboi
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    ECML
    2005
    Springer
    154views Machine Learning» more  ECML 2005»
    U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models
    14 years 6 months ago
    U-Likelihood and U-Updating Algorithms: Statistical Inference in Latent Variable Models
    Download  eprints.pascal-network.org
    Abstract. In this paper we consider latent variable models and introduce a new U-likelihood concept for estimating the distribution over hidden variables. One can derive an estimat...
    JaeMo Sung, Sung Yang Bang, Seungjin Choi, Zoubin ...
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    ICASSP
    2008
    IEEE
    139views Signal Processing» more  ICASSP 2008»
    A GIS-like training algorithm for log-linear models with hidden variables
    14 years 6 months ago
    A GIS-like training algorithm for log-linear models with hidden variables
    Download  thomas.deselaers.de
    Conditional Random Fields (CRFs) are often estimated using an entropy based criterion in combination with Generalized Iterative Scaling (GIS). GIS offers, upon others, the immedi...
    Georg Heigold, Thomas Deselaers, Ralf Schlüte...
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    ICASSP
    2008
    IEEE
    204views Signal Processing» more  ICASSP 2008»
    Maximum entropy relaxation for multiscale graphical model selection
    14 years 6 months ago
    Maximum entropy relaxation for multiscale graphical model selection
    Download  www.mit.edu
    We consider the problem of learning multiscale graphical models. Given a collection of variables along with covariance specifications for these variables, we introduce hidden var...
    Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...
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    ICML
    2005
    IEEE
    104views Machine Learning» more  ICML 2005»
    New d-separation identification results for learning continuous latent variable models
    15 years 1 months ago
    New d-separation identification results for learning continuous latent variable models
    Download  www.machinelearning.org
    Learning the structure of graphical models is an important task, but one of considerable difficulty when latent variables are involved. Because conditional independences using hid...
    Ricardo Silva, Richard Scheines
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