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    AAAI
    2006
    143views Intelligent Agents» more  AAAI 2006»
    Using an Ontology for Knowledge Acquisition
    13 years 11 months ago
    Using an Ontology for Knowledge Acquisition
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    We describe an approach to distributed knowledge acquisition using an ontology. The ontology is used to represent and reason about soldier performance. These methods are embedded ...
    Stacy Lovell, Webb Stacy
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    FLAIRS
    2004
    183views Artificial Intelligence» more  FLAIRS 2004»
    An Application of Neural Networks to Sequence Analysis and Genre Identification
    13 years 11 months ago
    An Application of Neural Networks to Sequence Analysis and Genre Identification
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    This study borrowed sequence analysis techniques from the genetic sciences and applied them to a similar problem in email filtering and web searching. Genre identification is the ...
    David Bisant
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    DAIS
    2003
    111views Distributed And Parallel Com...» more  DAIS 2003»
    DataWarp: Building Applications Which Make Progress in an Inconsistent World
    13 years 11 months ago
    DataWarp: Building Applications Which Make Progress in an Inconsistent World
    Download  users.ecs.soton.ac.uk
    Some of the data available to today’s enterprise applications is inconsistent. This is an inevitable consequence of data being distributed and partially replicated in many locati...
    Peter Henderson, Robert John Walters, Stephen Crou...
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    EUSFLAT
    2003
    131views Fuzzy Logic» more  EUSFLAT 2003»
    From communication networks to fuzzy sets
    13 years 11 months ago
    From communication networks to fuzzy sets
    Download  www.eusflat.org
    In 1931 and 1935, Ernst Adolph Guillemin, professor of electrical engineering at Masachusetts Institute of Technology (MIT), published the two volumes of his book “Communication...
    Rudolf Seising
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    SDM
    2003
    SIAM
    184views Data Mining» more  SDM 2003»
    Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
    13 years 11 months ago
    Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data
    Download  www-users.cs.umn.edu
    The problem of finding clusters in data is challenging when clusters are of widely differing sizes, densities and shapes, and when the data contains large amounts of noise and out...
    Levent Ertöz, Michael Steinbach, Vipin Kumar
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