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How Many Neighbors To Consider in Pattern Pre-selection for Support Vector Classifiers?

2003

Conference Paper

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Training support vector classifiers (SVC) requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVC training, we previously proposed a preprocessing algorithm which selects only the patterns in the overlap region around the decision boundary, based on neighborhood properties [8], [9], [10]. The k-nearest neighbors’ class label entropy for each pattern was used to estimate the pattern’s proximity to the decision boundary. The value of parameter k is critical, yet has been determined by a rather ad-hoc fashion. We propose in this paper a systematic procedure to determine k and show its effectiveness through experiments.

Author(s): Shin, H. and Cho, S.
Journal: Proc. of INNS-IEEE International Joint Conference on Neural Networks (IJCNN 2003)
Pages: 565-570
Year: 2003
Month: July
Day: 0

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

Event Name: IJCNN 2003
Event Place: Portland, Oregon, U.S.A.,

Digital: 0
Institution: Seoul National University, Seoul, Korea
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF

BibTex

@inproceedings{2710,
  title = {How Many Neighbors To Consider in Pattern Pre-selection for Support Vector Classifiers?},
  author = {Shin, H. and Cho, S.},
  journal = {Proc. of INNS-IEEE International Joint Conference on Neural Networks (IJCNN 2003)},
  pages = {565-570},
  organization = {Max-Planck-Gesellschaft},
  institution = {Seoul National University, Seoul, Korea},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2003},
  month_numeric = {7}
}