Support Vector Based Prototype Selection Method for Nearest Neighbor Rules

  • Yuangui Li
  • Zhonghui Hu
  • Yunze Cai
  • Weidong Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3610)


The Support vector machines derive the class decision hyper planes from a few, selected prototypes, the support vectors (SVs) according to the principle of structure risk minimization, so they have good generalization ability. We proposed a new prototype selection method based on support vectors for nearest neighbor rules. It selects prototypes only from support vectors. During classification, for unknown example, it can be classified into the same class as the nearest neighbor in feature space among all the prototypes. Computational results show that our method can obtain higher reduction rate and accuracy than popular condensing or editing instance reduction method.


Support Vector Machine Support Vector Feature Space Near Neighbor Linear Support Vector Machine 
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 2005

Authors and Affiliations

  • Yuangui Li
    • 1
  • Zhonghui Hu
    • 1
  • Yunze Cai
    • 1
  • Weidong Zhang
    • 1
  1. 1.Department of AutomationShanghai Jiaotong UniversityXuhui, ShanghaiP. R. China

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