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    NIPS
    1998
    138views Information Technology» more  NIPS 1998»
    Dynamically Adapting Kernels in Support Vector Machines
    13 years 10 months ago
    Dynamically Adapting Kernels in Support Vector Machines
    Download  www.support-vector.net
    The kernel-parameter is one of the few tunable parameters in Support Vector machines, controlling the complexity of the resulting hypothesis. Its choice amounts to model selection...
    Nello Cristianini, Colin Campbell, John Shawe-Tayl...
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    NIPS
    2000
    148views Information Technology» more  NIPS 2000»
    Active Support Vector Machine Classification
    13 years 10 months ago
    Active Support Vector Machine Classification
    Download  www.iipl.fudan.edu.cn
    An active set strategy is applied to the dual of a simple reformulation of the standard quadratic program of a linear support vector machine. This application generates a fast new...
    Olvi L. Mangasarian, David R. Musicant
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    ICPR
    2010
    IEEE
    243views Computer Vision» more  ICPR 2010»
    Adaptive Incremental Learning with an Ensemble of Support Vector Machines
    13 years 9 months ago
    Adaptive Incremental Learning with an Ensemble of Support Vector Machines
    Download  www.livia.etsmtl.ca
    The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incre...
    Marcelo N. Kapp, Robert Sabourin, Patrick Maupin
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    BMCBI
    2006
    106views more  BMCBI 2006»
    Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector
    13 years 9 months ago
    Prediction of the functional class of metal-binding proteins from sequence derived physicochemical properties by support vector
    Download  www.biomedcentral.com
    Metal-binding proteins play important roles in structural stability, signaling, regulation, transport, immune response, metabolism control, and metal homeostasis. Because of their...
    H. H. Lin, L. Y. Han, H. L. Zhang, C. J. Zheng, B....
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    JMLR
    2008
    110views more  JMLR 2008»
    Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
    13 years 9 months ago
    Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
    Download  jmlr.csail.mit.edu
    Support vector machine (SVM) is one of the most popular and promising classification algorithms. After a classification rule is constructed via the SVM, it is essential to evaluat...
    Bo Jiang, Xuegong Zhang, Tianxi Cai
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