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A DC programming approach for feature selection in support vector machines learning

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Author Info

  • Hoai Le Thi

    ()

  • Hoai Le

    ()

  • Van Nguyen

    ()

  • Tao Pham Dinh

    ()

Abstract

No abstract is available for this item.

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File URL: http://hdl.handle.net/10.1007/s11634-008-0030-7
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Bibliographic Info

Article provided by Springer in its journal Advances in Data Analysis and Classification.

Volume (Year): 2 (2008)
Issue (Month): 3 (December)
Pages: 259-278

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Handle: RePEc:spr:advdac:v:2:y:2008:i:3:p:259-278

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Web page: http://www.springer.com/statistics/statistical+theory+and+methods/journal/11634

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Related research

Keywords: Feature selection; SVM; Nonconvex optimisation; DC programming; DCA; 90C26; 62-07;

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References

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  1. Hoai An, Le Thi & Minh, Le Hoai & Tao, Pham Dinh, 2007. "Optimization based DC programming and DCA for hierarchical clustering," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1067-1085, December.
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Citations

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Cited by:
  1. Guan, Wei & Gray, Alexander, 2013. "Sparse high-dimensional fractional-norm support vector machine via DC programming," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 136-148.
  2. Paola Cerchiello & Paolo Giudici, 2012. "Non parametric statistical models for on-line text classification," Advances in Data Analysis and Classification, Springer, vol. 6(4), pages 277-288, December.
  3. Hoai Le Thi & Tao Pham Dinh & Huynh Ngai, 2012. "Exact penalty and error bounds in DC programming," Journal of Global Optimization, Springer, vol. 52(3), pages 509-535, March.
  4. Liming Yang & Laisheng Wang, 2013. "A class of semi-supervised support vector machines by DC programming," Advances in Data Analysis and Classification, Springer, vol. 7(4), pages 417-433, December.

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