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    ESANN
    2007
    187views Neural Networks» more  ESANN 2007»
    How to process uncertainty in machine learning?
    13 years 8 months ago
    How to process uncertainty in machine learning?
    Download  www.dice.ucl.ac.be
    Uncertainty is a popular phenomenon in machine learning and a variety of methods to model uncertainty at different levels has been developed. The aim of this paper is to motivate ...
    Barbara Hammer, Thomas Villmann
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    MIE
    2008
    119views Healthcare» more  MIE 2008»
    Systematizing medical alerts
    13 years 8 months ago
    Systematizing medical alerts
    Download  person.hst.aau.dk
    The current Swedish regulations for medical alerts in health records were designed for paper records. Suggestions for computerized systems are now being investigated. A proposed mo...
    Mattias Pettersson, Jenny Wihlborg, Rikard Lö...
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    IQ
    2007
    93views Information Technology» more  IQ 2007»
    In Search Of An Accuracy Metric
    13 years 8 months ago
    In Search Of An Accuracy Metric
    Download  mitiq.mit.edu
    Practitioners and researchers often refer to error rates or accuracy percentages of databases. The former is the number of cells in error divided by the total number of cells; the...
    Craig W. Fisher, Eitel J. M. Lauría, Caroly...
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    TRECVID
    2008
    200views Internet Technology» more  TRECVID 2008»
    National Institute of Informatics, Japan at TRECVID 2008
    13 years 8 months ago
    National Institute of Informatics, Japan at TRECVID 2008
    Download  www-nlpir.nist.gov
    This paper reports our experiments for TRECVID 2008 tasks: high level feature extraction, search and contentbased copy detection. For the high level feature extraction task, we use...
    Duy-Dinh Le, Xiaomeng Wu, Shin'ichi Satoh, Sheetal...
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    ACL
    2006
    161views Computational Linguistics» more  ACL 2006»
    Minimum Risk Annealing for Training Log-Linear Models
    13 years 8 months ago
    Minimum Risk Annealing for Training Log-Linear Models
    Download  www.cs.jhu.edu
    When training the parameters for a natural language system, one would prefer to minimize 1-best loss (error) on an evaluation set. Since the error surface for many natural languag...
    David A. Smith, Jason Eisner
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