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Keerthi, S. and Gilbert, E. (2002) Convergence of a Generalized SMO Algorithm for SVM Classifier Design. Machine Learning, 46, 351-360.
https://doi.org/10.1023/A:1012431217818

has been cited by the following article:

• TITLE: A Fast Algorithm for Training Large Scale Support Vector Machines

  AUTHORS: Mayowa Kassim Aregbesola, Igor Griva

  KEYWORDS: SVM, Machine Learning, Support Vector Machines, FISTA, Fast Projected Gradient, Augmented Lagrangian, Working Set Selection, Decomposition

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.12, December 16, 2022

  ABSTRACT: The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classification machines (SVM). The manuscript describes the ALFPGM algorithm, provides numerical results for training SVM on large data sets, and compares the training times of ALFPGM and Sequential Minimal Minimization algorithms (SMO) from Scikit-learn library. The numerical results demonstrate that ALFPGM with the improved working selection scheme is capable of training SVM with tens of thousands of training examples in a fraction of the training time of some widely adopted SVM tools.