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    COLING
    1996
    92views Computational Linguistics» more  COLING 1996»
    FeasPar - A Feature Structure Parser Learning to Parse Spoken Language
    13 years 9 months ago
    FeasPar - A Feature Structure Parser Learning to Parse Spoken Language
    Download  acl.ldc.upenn.edu
    We describe and experimentally evaluate a system, FeasPar, that learns parsing spontaneous speech. To train and run FeasPar (Feature Structure Parser), only limited handmodeled kn...
    Finn Dag Buø, Alex Waibel
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    CVPR
    2009
    IEEE
    330views Computer Vision» more  CVPR 2009»
    Discriminative Structure Learning of Hierarchical Representations for Object Detection
    15 years 3 months ago
    Discriminative Structure Learning of Hierarchical Representations for Object Detection
    Download  www.mis.informatik.tu-darmstadt.de
    A variety of flexible models have been proposed to detect objects in challenging real world scenes. Motivated by some of the most successful techniques, we propose a hierarchica...
    Paul Schnitzspan (TU Darmstadt), Mario Fritz (Univ...
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    ICML
    2009
    IEEE
    114views Machine Learning» more  ICML 2009»
    Structure learning with independent non-identically distributed data
    14 years 9 months ago
    Structure learning with independent non-identically distributed data
    Download  www.andrew.cmu.edu
    There are well known algorithms for learning the structure of directed and undirected graphical models from data, but nearly all assume that the data consists of a single i.i.d. s...
    Robert E. Tillman
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    ML
    2006
    ACM
    142views Machine Learning» more  ML 2006»
    The max-min hill-climbing Bayesian network structure learning algorithm
    13 years 8 months ago
    The max-min hill-climbing Bayesian network structure learning algorithm
    Download  www.dsl-lab.org
    We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
    Ioannis Tsamardinos, Laura E. Brown, Constantin F....
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    ACCV
    2010
    Springer
    175views Computer Vision» more  ACCV 2010»
    Learning Image Structures for Optimizing Disparity Estimation
    13 years 3 months ago
    Learning Image Structures for Optimizing Disparity Estimation
    Download  vims.cis.udel.edu
    We present a method for optimizing the stereo matching process when it is applied to a series of images with similar depth structures. We observe that there are similar regions wit...
    M. V. Rohith, Chandra Kambhamettu
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