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    Massive Data Classification via Unconstrained Support Vector Machines

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    Massive Data Classification via Unconstrained Support Vector Machines (114.3Kb)
    Date
    2006
    Author
    Thompson, Michael
    Mangasarian, Olvi
    Metadata
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    Abstract
    A highly accurate algorithm, based on support vector machines formulated as linear programs [13, 1], is proposed here as a completely unconstrained minimization problem [15]. Combined with a chunking procedure [2] this approach, which requires nothing more complex than a linear equation solver, leads to a simple and accurate method for classifying million-point datasets. Because a 1-norm support vector machine underlies the proposed approach, the method suppresses input space features as well. A state-of-the-art linear programming package, CPLEX [10], fails to solve problems handled by the proposed algorithm.
    Subject
    linear program
    massive data classification
    support vector machines
    Permanent Link
    http://digital.library.wisc.edu/1793/64336
    Citation
    06-01
    Part of
    • DMI Technical Reports

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