Header logo is ei

Pattern Selection for Support Vector Classifiers


Conference Paper


SVMs tend to take a very long time to train with a large data set. If "redundant" patterns are identified and deleted in pre-processing, the training time could be reduced significantly. We propose a k-nearest neighbors(k-NN) based pattern selection method. The method tries to select the patterns that are near the decision boundary and that are correctly labeled. The simulations over synthetic data sets showed promising results: (1) By converting a non-separable problem to a separable one, the search for an optimal error tolerance parameter became unnecessary. (2) SVM training time decreased by two orders of magnitude without any loss of accuracy. (3) The redundant SVs were substantially reduced.

Author(s): Shin, H. and Cho, S.
Book Title: Ideal 2002
Journal: Intelligent Data Engineering and Automated Learning (IDEAL 2002)
Pages: 97-103
Year: 2002
Month: January
Day: 0
Editors: Yin, H. , N. Allinson, R. Freeman, J. Keane, S. Hubbard
Publisher: Springer

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1007/3-540-45675-9_70
Event Name: Third International Conference on Intelligent Data Engineering and Automated Learning
Event Place: Manchester, United Kingdom

Address: Berlin, Germany
Digital: 0
Institution: Seoul National University, Seoul, Korea
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Pattern Selection for Support Vector Classifiers},
  author = {Shin, H. and Cho, S.},
  journal = {Intelligent Data Engineering and Automated Learning (IDEAL 2002)},
  booktitle = {Ideal 2002},
  pages = {97-103},
  editors = {Yin, H. , N. Allinson, R. Freeman, J. Keane, S. Hubbard},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  institution = {Seoul National University, Seoul, Korea},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = jan,
  year = {2002},
  month_numeric = {1}