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 |
Links: |
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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}, doi = {10.1007/3-540-36175-8_37}, month_numeric = {5} } |