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Support Vector Machine Classification Based on Fuzzy Clustering for Large Data Sets

  • Jair Cervantes
  • Xiaoou Li
  • Wen Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4293)

Abstract

Support vector machine (SVM) has been successfully applied to solve a large number of classification problems. Despite its good theoretic foundations and good capability of generalization, it is a big challenging task for the large data sets due to the training complexity, high memory requirements and slow convergence. In this paper, we present a new method, SVM classification based on fuzzy clustering. Before applying SVM we use fuzzy clustering, in this stage the optimal number of clusters are not needed in order to have less computational cost. We only need to partition the training data set briefly. The SVM classification is realized with the center of the groups. Then the de-clustering and SVM classification via reduced data are used. The proposed approach is scalable to large data sets with high classification accuracy and fast convergence speed. Empirical studies show that the proposed approach achieves good performance for large data sets.

Keywords

Support Vector Machine Fuzzy Cluster Quadratic Programming Problem Data Subset Membership Grade 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jair Cervantes
    • 1
  • Xiaoou Li
    • 1
  • Wen Yu
    • 2
  1. 1.Sección de Computación Departamento de Ingenierá Elétrica, CINVESTAV-IPNMéxico D.F.México
  2. 2.Departamento de Control Automático, CINVESTAV-IPNMéxico D.F.México

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