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    SMO-based pruning methods for sparse least squares support vector machines

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    Issue Date
    2005-11
    Author
    Zeng, Xiangyan
    Chen, Xue-wen
    Publisher
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
    Type
    Article
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    Abstract
    Solutions of least squares support vector machines (LS-SVMs) are typically nonsparse. The sparseness is imposed by subsequently omitting data that introduce the smallest training errors and retraining the remaining data. Iterative retraining requires more intensive computations than training a single nonsparse LS-SVM. In this paper, we propose a new pruning algorithm for sparse LS-SVMs: the sequential minimal optimization (SMO) method is introduced into pruning process; in addition, instead of determining the pruning points by errors, we omit the data points that will introduce minimum changes to a dual objective function. This new criterion is computationally efficient. The effectiveness of the proposed method in terms of computational cost and classification accuracy is demonstrated by numerical experiments.
    URI
    http://hdl.handle.net/1808/1512
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    • Electrical Engineering and Computer Science Scholarly Works [302]
    Citation
    Zeng, XY; Chen, XW. SMO-based pruning methods for sparse least squares support vector machines. IEEE TRANSACTIONS ON NEURAL NETWORKS. November 2005. 16(6) : 1541-1546

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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