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

  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},
  • 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
Highly Cited
This paper has 38 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 20 extracted citations


Publications referenced by this paper.
Showing 1-10 of 21 references

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
  • 2008

Similar Papers

Loading similar papers…