Mining Very Large Datasets with Support Vector Machine Algorithms

  • François Poulet
  • Thanh-Nghi Do
Conference paper


In this paper, we present new support vector machines (SVM) algorithms that can be used to classify very large datasets on standard personal computers. The algorithms have been extended from three recent SVMs algorithms: least squares SVM classification, finite Newton method for classification and incremental proximal SVM classification. The extension consists in building incremental, parallel and distributed SVMs for classification. Our three new algorithms are very fast and can handle very large datasets. An example of the effectiveness of these new algorithms is given with the classification into two classes of one billion points in 10-dimensional input space in some minutes on ten personal computers (800 MHz Pentium III, 256 MB RAM, Linux).


Data mining Parallel and distributed algorithms Classification Machine learning Support vector machines Least squares classifiers Newton method Proximal classifiers Incremental learning 


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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • François Poulet
    • 1
  • Thanh-Nghi Do
    • 1
  1. 1.ESIEA RechercheLavalFrance

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