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    BMCBI
    2007
    104views more  BMCBI 2007»
    Joint mapping of genes and conditions via multidimensional unfolding analysis
    13 years 7 months ago
    Joint mapping of genes and conditions via multidimensional unfolding analysis
    Download  www.biomedcentral.com
    Background: Microarray compendia profile the expression of genes in a number of experimental conditions. Such data compendia are useful not only to group genes and conditions base...
    Katrijn Van Deun, Kathleen Marchal, Willem J. Heis...
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    ICDM
    2009
    IEEE
    233views Data Mining» more  ICDM 2009»
    Semi-Supervised Sequence Labeling with Self-Learned Features
    14 years 2 months ago
    Semi-Supervised Sequence Labeling with Self-Learned Features
    Download  www.cs.cmu.edu
    —Typical information extraction (IE) systems can be seen as tasks assigning labels to words in a natural language sequence. The performance is restricted by the availability of l...
    Yanjun Qi, Pavel Kuksa, Ronan Collobert, Kunihiko ...
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    SIGMOD
    2008
    ACM
    139views Database» more  SIGMOD 2008»
    SQAK: doing more with keywords
    14 years 7 months ago
    SQAK: doing more with keywords
    Download  www-db.deis.unibo.it
    Today's enterprise databases are large and complex, often relating hundreds of entities. Enabling ordinary users to query such databases and derive value from them has been o...
    Sandeep Tata, Guy M. Lohman
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    MKWI
    2008
    136views Business» more  MKWI 2008»
    KASIMIR - Work process embedded task management leveraging the Semantic Desktop
    13 years 9 months ago
    KASIMIR - Work process embedded task management leveraging the Semantic Desktop
    Download  ibis.in.tum.de
    : The effectiveness of semantic applications can be increased significantly if the system does not urge the users to take care of explicit semantic annotation creation. Today, sema...
    Olaf Grebner, Ernie Ong, Uwe V. Riss
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    ICML
    2005
    IEEE
    165views Machine Learning» more  ICML 2005»
    Supervised dimensionality reduction using mixture models
    14 years 8 months ago
    Supervised dimensionality reduction using mixture models
    Download  talk.ucsd.edu
    Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We...
    Sajama, Alon Orlitsky
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