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    NAACL
    2007
    142views Computational Linguistics» more  NAACL 2007»
    Semi-Supervised Learning for Semantic Parsing using Support Vector Machines
    13 years 11 months ago
    Semi-Supervised Learning for Semantic Parsing using Support Vector Machines
    Download  www.cs.utexas.edu
    We present a method for utilizing unannotated sentences to improve a semantic parser which maps natural language (NL) sentences into their formal meaning representations (MRs). Gi...
    Rohit J. Kate, Raymond J. Mooney
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    ICCV
    2007
    IEEE
    224views Computer Vision» more  ICCV 2007»
    Co-Tracking Using Semi-Supervised Support Vector Machines
    14 years 4 months ago
    Co-Tracking Using Semi-Supervised Support Vector Machines
    Download  www.klab.caltech.edu
    This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...
    Feng Tang, Shane Brennan, Qi Zhao, Hai Tao
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    LREC
    2008
    150views Education» more  LREC 2008»
    Automatic Translation of Biomedical Terms by Supervised Machine Learning
    13 years 11 months ago
    Automatic Translation of Biomedical Terms by Supervised Machine Learning
    Download  www.lrec-conf.org
    In this paper, we present a simple yet efficient automatic system to translate biomedical terms. It mainly relies on a machine learning approach able to infer rewriting rules from...
    Vincent Claveau
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    BIOCOMP
    2007
    161views Bioinformatics» more  BIOCOMP 2007»
    Biomarker Discovery Across Annotated and Unannotated Microarray Datasets Using Semi-Supervised Learning
    13 years 11 months ago
    Biomarker Discovery Across Annotated and Unannotated Microarray Datasets Using Semi-Supervised Learning
    Download  www.biomedcentral.com
    The growing body of DNA microarray data has the potential to advance our understanding of the molecular basis of disease. However annotating microarray datasets with clinically us...
    Cole Harris, Noushin Ghaffari
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    ICONIP
    2009
    153views Information Technology» more  ICONIP 2009»
    Learning Gaussian Process Models from Uncertain Data
    13 years 7 months ago
    Learning Gaussian Process Models from Uncertain Data
    Download  damas.ift.ulaval.ca
    It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
    Patrick Dallaire, Camille Besse, Brahim Chaib-draa
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