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Neighborhood Property based Pattern Selection for Support Vector Machines




The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training.

Author(s): Shin, H. and Cho, S.
Journal: Neural Computation
Volume: 19
Number (issue): 3
Pages: 816-855
Year: 2007
Month: March
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1162/neco.2007.19.3.816
Institution: Seoul National University, Seoul, Korea
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

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  title = {Neighborhood Property based Pattern Selection for Support Vector Machines},
  author = {Shin, H. and Cho, S.},
  journal = {Neural Computation},
  volume = {19},
  number = {3},
  pages = {816-855},
  organization = {Max-Planck-Gesellschaft},
  institution = {Seoul National University, Seoul, Korea},
  school = {Biologische Kybernetik},
  month = mar,
  year = {2007},
  month_numeric = {3}