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


Conference Paper


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


  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},
  month_numeric = {9}