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A Comparative Study on Improved Fuzzy Support Vector Machines and Levenberg-Marquardt-Based BP Network

  • Chao-feng Li
  • Lei Xu
  • Shi-tong Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

The paper proposes an edge-effect training multi-class fuzzy support vector machine (EFSVM). It treats the training data points with different importance in the training process, and especially emphasizes primary contribution of these points distributed in edge area of data sets for classification, and then assigns them greater fuzzy membership degrees, thus assures that the nearer these points are away from edge area of training sets and the greater their contribution are. At the same time EFSVM is systematically compared to two other fuzzy support vector machines and a Levenberg-Marquardt-based BP algorithm (LMBP). The classification results for both Iris data and remote sensing image show that EFSVM is the best and may effectively enhance pattern classification accuracy.

Keywords

Support Vector Machine Edge Area Remote Sensing Image Training Data Point Fuzzy 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 2006

Authors and Affiliations

  • Chao-feng Li
    • 1
  • Lei Xu
    • 2
  • Shi-tong Wang
    • 1
  1. 1.School of Information TechnologySouthern Yangtze UniversityWuxiChina
  2. 2.Computer DepartmentLuoyang Normal UniversityLuoyangChina

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