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Fast Pattern Selection Algorithm for Support Vector Classifiers: "Time Complexity Analysis"

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. The time complexity of the proposed algorithm is much smaller than that of the naive M^2 algorithm

Author(s): Shin, H. and Cho, S.
Journal: Lecture Notes in Computer Science (LNCS 2690)
Volume: LNCS 2690
Pages: 1008-1015
Year: 2003
Month: September
Day: 0
Publisher: Springer-Verlag

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

Event Name: The 4th International Conference on Intelligent Data Engineering (IDEAL)
Event Place: Hong Kong, China

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

Links: PDF

BibTex

@inproceedings{2694,
  title = {Fast Pattern Selection Algorithm for Support Vector Classifiers: "Time Complexity Analysis"},
  author = {Shin, H. and Cho, S.},
  journal = {Lecture Notes in Computer Science (LNCS 2690)},
  volume = {LNCS 2690},
  pages = {1008-1015},
  publisher = {Springer-Verlag},
  organization = {Max-Planck-Gesellschaft},
  institution = {Seoul National University, Seoul, Korea},
  school = {Biologische Kybernetik},
  address = {Heidelberg},
  month = sep,
  year = {2003},
  doi = {},
  month_numeric = {9}
}