A Simple and Effective Lagrangian-Based Combinatorial Algorithm for S\(^3\)VMs

  • Francesco BagattiniEmail author
  • Paola Cappanera
  • Fabio Schoen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)


Many optimization techniques have been developed in the last decade to include the unlabeled patterns in the Support Vector Machines formulation. Two broad strategies are followed: continuous and combinatorial. The approach presented in this paper belongs to the latter family and is especially suitable when a fair estimation of the proportion of positive and negative samples is available. Our method is very simple and requires a very light parameter selection. Experiments on both artificial and real-world datasets have been carried out, proving the effectiveness and the efficiency of the proposed algorithm.


Semi-supervised learning Support vector machines Lagrangian combinatorial heuristics 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Francesco Bagattini
    • 1
    Email author
  • Paola Cappanera
    • 1
  • Fabio Schoen
    • 1
  1. 1.DINFOUniversità degli Studi di FirenzeFirenzeItaly

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