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    IJON
    2010
    148views more  IJON 2010»
    Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machin
    13 years 6 months ago
    Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machin
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    Several solutions have been proposed to exploit the availability of heterogeneous sources of biomolecular data for gene function prediction, but few attention has been dedicated t...
    Matteo Re, Giorgio Valentini
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    ACL
    2008
    145views Computational Linguistics» more  ACL 2008»
    Name Translation in Statistical Machine Translation - Learning When to Transliterate
    13 years 10 months ago
    Name Translation in Statistical Machine Translation - Learning When to Transliterate
    Download  www.isi.edu
    We present a method to transliterate names in the framework of end-to-end statistical machine translation. The system is trained to learn when to transliterate. For Arabic to Engl...
    Ulf Hermjakob, Kevin Knight, Hal Daumé III
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    BMCBI
    2008
    123views more  BMCBI 2008»
    Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach
    13 years 9 months ago
    Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach
    Download  www.biomedcentral.com
    Background: Eukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing a...
    Firoz Anwar, Syed Murtuza Baker, Taskeed Jabid, Md...
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    CVPR
    2008
    IEEE
    194views Computer Vision» more  CVPR 2008»
    Learning for stereo vision using the structured support vector machine
    14 years 11 months ago
    Learning for stereo vision using the structured support vector machine
    Download  www.cs.cornell.edu
    We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
    Yunpeng Li, Daniel P. Huttenlocher
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    ACL
    2007
    125views Computational Linguistics» more  ACL 2007»
    A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
    13 years 10 months ago
    A Seed-driven Bottom-up Machine Learning Framework for Extracting Relations of Various Complexity
    Download  www.dfki.de
    A minimally supervised machine learning framework is described for extracting relations of various complexity. Bootstrapping starts from a small set of n-ary relation instances as...
    Feiyu Xu, Hans Uszkoreit, Hong Li
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