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Constructing a Robust Least Squares Support Vector Machine Based on Lp-norm and L∞-norm

Jahanmard Hosseinabadi, Maryam | 2022

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 55160 (02)
  4. University: Sharif University of Technolog
  5. Department: Mathematical Sciences
  6. Advisor(s): Mahdavi Amiri, Nezamoddin
  7. Abstract:
  8. In the last decades, Support Vector Machine (SVM) has been used for supervised classification due to its wide applications. In addition, SVM is a mathematical programming tool, that as other optimization-based approaches, turns to be useful for a successful development of supervised classification. Based on current research in the literature, we explain the extension of a developed method for SVM using L2-norm to the more general case of Lp-norms with p > 1. The Kernel function is used frequently in the L2-SVM model, but the multidimensional Kernel is used as a general function in Lp-SVM. Finally, we use Lp-LSSVM model, with 0
  9. Keywords:
  10. Support Vector Machine (SVM) ; Mathematical Optimization ; Supervised Learning ; Least Squares Method ; Sequential Minimal Optimization (SMO)

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