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    SDM
    2009
    SIAM
    112views Data Mining» more  SDM 2009»
    A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.
    15 years 7 months ago
    A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.
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    Most commonly used inductive rule learning algorithms employ a hill-climbing search, whereas local pattern discovery algorithms employ exhaustive search. In this paper, we evaluat...
    Frederik Janssen, Johannes Fürnkranz
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    SDM
    2009
    SIAM
    126views Data Mining» more  SDM 2009»
    An Entity Based Model for Coreference Resolution.
    15 years 7 months ago
    An Entity Based Model for Coreference Resolution.
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    Recently, many advanced machine learning approaches have been proposed for coreference resolution; however, all of the discriminatively-trained models reason over mentions rather ...
    Michael L. Wick, Aron Culotta, Khashayar Rohaniman...
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    SDM
    2009
    SIAM
    125views Data Mining» more  SDM 2009»
    Providing Privacy through Plausibly Deniable Search.
    15 years 7 months ago
    Providing Privacy through Plausibly Deniable Search.
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    Query-based web search is an integral part of many people’s daily activities. Most do not realize that their search history can be used to identify them (and their interests). I...
    Chris Clifton, Mummoorthy Murugesan
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    SDM
    2009
    SIAM
    140views Data Mining» more  SDM 2009»
    Straightforward Feature Selection for Scalable Latent Semantic Indexing.
    15 years 7 months ago
    Straightforward Feature Selection for Scalable Latent Semantic Indexing.
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    Latent Semantic Indexing (LSI) has been validated to be effective on many small scale text collections. However, little evidence has shown its effectiveness on unsampled large sca...
    Jun Yan, Shuicheng Yan, Ning Liu, Zheng Chen
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    SDM
    2009
    SIAM
    220views Data Mining» more  SDM 2009»
    Bayesian Cluster Ensembles.
    15 years 7 months ago
    Bayesian Cluster Ensembles.
    Download  www-users.cs.umn.edu
    Cluster ensembles provide a framework for combining multiple base clusterings of a dataset to generate a stable and robust consensus clustering. There are important variants of th...
    Hongjun Wang, Hanhuai Shan, Arindam Banerjee
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