New Primal SVM Solver with Linear Computational Cost for Big Data Classifications

@inproceedings{FEIPINGNIE2014NewPS,
  title={New Primal SVM Solver with Linear Computational Cost for Big Data Classifications},
  author={FEIPINGNIE and GMAIL. COM and HUANG. YIZHEN and Xinhua Wang and MAVS. UTA. EDU and Heng},
  year={2014}
}
  • FEIPINGNIE, GMAIL. COM, +3 authors Heng
  • Published 2014
Support Vector Machines (SVM) is among the most popular classification techniques in machine learning, hence designing fast primal SVM algorithms for large-scale datasets is a hot topic in recent years. This paper presents a new L2norm regularized primal SVM solver using Augmented Lagrange Multipliers, with linear computational cost for Lp-norm loss functions. The most computationally intensive steps (that determine the algorithmic complexity) of the proposed algorithm is purely and simply… CONTINUE READING
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A primaldual augmented lagrangian

  • E Philip, P. Daniel
  • Uci machine learning repository
  • 2010

A comparison of methods for multi-class support vector machines

  • Hsu, Chih-Wei, Lin, Chih-Jen
  • IEEE TNN
  • 2008

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