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    ESANN
    2008
    90views Neural Networks» more  ESANN 2008»
    Comparison of sparse least squares support vector regressors trained in primal and dual
    15 years 5 months ago
    Comparison of sparse least squares support vector regressors trained in primal and dual
    Download  www.dice.ucl.ac.be
    In our previous work, we have developed sparse least squares support vector regressors (sparse LS SVRs) trained in the primal form in the reduced empirical feature space. In this p...
    Shigeo Abe
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    143
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    ICDM
    2009
    IEEE
    172views Data Mining» more  ICDM 2009»
    Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework
    15 years 10 months ago
    Sparse Least-Squares Methods in the Parallel Machine Learning (PML) Framework
    Download  www.cse.ohio-state.edu
    —We describe parallel methods for solving large-scale, high-dimensional, sparse least-squares problems that arise in machine learning applications such as document classificatio...
    Ramesh Natarajan, Vikas Sindhwani, Shirish Tatikon...
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    CISS
    2008
    IEEE
    110views Information Technology» more  CISS 2008»
    Iteratively Re-weighted Least Squares minimization: Proof of faster than linear rate for sparse recovery
    15 years 5 months ago
    Iteratively Re-weighted Least Squares minimization: Proof of faster than linear rate for sparse recovery
    Download  conf.ee.princeton.edu
    Ingrid Daubechies, Ronald A. DeVore, Massimo Forna...
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    133
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    CORR
    2004
    Springer
    229views Education» more  CORR 2004»
    Tsnnls: A solver for large sparse least squares problems with non-negative variables
    15 years 3 months ago
    Tsnnls: A solver for large sparse least squares problems with non-negative variables
    Download  www.cs.washington.edu
    Jason Cantarella, Michael Piatek
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    PAA
    2007
    91views Distributed And Parallel Com...» more  PAA 2007»
    Sparse least squares support vector training in the reduced empirical feature space
    15 years 3 months ago
    Sparse least squares support vector training in the reduced empirical feature space
    Download  www.lib.kobe-u.ac.jp
    Shigeo Abe
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