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    IEEEVAST
    2010
    171views Emerging Technology» more  IEEEVAST 2010»
    Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological opera
    12 years 11 months ago
    Finding and visualizing relevant subspaces for clustering high-dimensional astronomical data using connected morphological opera
    Download  iwi.eldoc.ub.rug.nl
    Data sets in astronomy are growing to enormous sizes. Modern astronomical surveys provide not only image data but also catalogues of millions of objects (stars, galaxies), each ob...
    Bilkis J. Ferdosi, Hugo Buddelmeijer, Scott Trager...
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    NAR
    2011
    159views Computer Vision» more  NAR 2011»
    SuperSweet - a resource on natural and artificial sweetening agents
    12 years 7 months ago
    SuperSweet - a resource on natural and artificial sweetening agents
    Download  nar.oxfordjournals.org
    A vast number of sweet tasting molecules are known, encompassing small compounds, carbohydrates, D-amino acids and large proteins. Carbohydrates play a particularly big role in hu...
    Jessica Ahmed, Saskia Preissner, Mathias Dunkel, C...
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    NAR
    2011
    223views Computer Vision» more  NAR 2011»
    ODB: a database for operon organizations, 2011 update
    12 years 7 months ago
    ODB: a database for operon organizations, 2011 update
    Download  nar.oxfordjournals.org
    ODB (Operon DataBase) aims to collect data of all known and conserved operons in completely sequenced genomes. Three newly updated features of this database have been added as fol...
    Shujiro Okuda, Akiyasu C. Yoshizawa
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    SIGMOD
    2011
    ACM
    250views Database» more  SIGMOD 2011»
    Hybrid in-database inference for declarative information extraction
    12 years 7 months ago
    Hybrid in-database inference for declarative information extraction
    Download  db.cs.berkeley.edu
    In the database community, work on information extraction (IE) has centered on two themes: how to effectively manage IE tasks, and how to manage the uncertainties that arise in th...
    Daisy Zhe Wang, Michael J. Franklin, Minos N. Garo...
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    SDM
    2012
    SIAM
    216views Data Mining» more  SDM 2012»
    Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
    11 years 7 months ago
    Feature Selection "Tomography" - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable
    Download  www.hpl.hp.com
    :  Feature Selection “Tomography” - Illustrating that Optimal Feature Filtering is Hopelessly Ungeneralizable George Forman HP Laboratories HPL-2010-19R1 Feature selection; ...
    George Forman
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