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Fast Pattern Selection for Support Vector Classifiers

2003

Conference Paper

ei


Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. Preliminary simulation results were promising: Up to two orders of magnitude, training time reduction was achieved including the preprocessing, without any loss in classification accuracies.

Author(s): Shin, H. and Cho, S.
Book Title: PAKDD 2003
Journal: Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference (PAKDD 2003)
Pages: 376-387
Year: 2003
Month: May
Day: 0
Editors: Whang, K.-Y. , J. Jeon, K. Shim, J. Srivastava
Publisher: Springer

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

DOI: 10.1007/3-540-36175-8_37
Event Name: 7th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Event Place: Seoul, Korea

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

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BibTex

@inproceedings{2693,
  title = {Fast Pattern Selection for Support Vector Classifiers},
  author = {Shin, H. and Cho, S.},
  journal = {Advances in Knowledge Discovery and Data Mining: 7th Pacific-Asia Conference (PAKDD 2003)},
  booktitle = {PAKDD 2003},
  pages = {376-387},
  editors = {Whang, K.-Y. , J. Jeon, K. Shim, J. Srivastava},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  institution = {Seoul National University, Seoul, Korea},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = may,
  year = {2003},
  month_numeric = {5}
}