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    COLING
    2008
    128views Computational Linguistics» more  COLING 2008»
    Robust and Efficient Chinese Word Dependency Analysis with Linear Kernel Support Vector Machines
    13 years 9 months ago
    Robust and Efficient Chinese Word Dependency Analysis with Linear Kernel Support Vector Machines
    Download  www.aclweb.org
    Data-driven learning based on shift reduce parsing algorithms has emerged dependency parsing and shown excellent performance to many Treebanks. In this paper, we investigate the e...
    Yu-Chieh Wu, Jie-Chi Yang, Yue-Shi Lee
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    JMLR
    2008
    81views more  JMLR 2008»
    Bouligand Derivatives and Robustness of Support Vector Machines for Regression
    13 years 7 months ago
    Bouligand Derivatives and Robustness of Support Vector Machines for Regression
    Download  homepages.vub.ac.be
    Andreas Christmann, Arnout Van Messem
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    PR
    2010
    163views more  PR 2010»
    Optimal feature selection for support vector machines
    13 years 6 months ago
    Optimal feature selection for support vector machines
    Download  www.andrew.cmu.edu
    Selecting relevant features for Support Vector Machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and ...
    Minh Hoai Nguyen, Fernando De la Torre
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    IJCNN
    2008
    IEEE
    139views Neural Networks» more  IJCNN 2008»
    Sparse support vector machines trained in the reduced empirical feature space
    14 years 2 months ago
    Sparse support vector machines trained in the reduced empirical feature space
    Download  www.lib.kobe-u.ac.jp
    — We discuss sparse support vector machines (sparse SVMs) trained in the reduced empirical feature space. Namely, we select the linearly independent training data by the Cholesky...
    Kazuki Iwamura, Shigeo Abe
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    ICPR
    2000
    IEEE
    152views computer vision» more  ICPR 2000»
    Feature Selection for Support Vector Machines
    14 years 8 months ago
    Feature Selection for Support Vector Machines
    Download  wwwmaths.anu.edu.au
    Lothar Hermes, Joachim M. Buhmann
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