Building Sparse Large Margin Classifiers
2005
Conference Paper
ei
This paper presents an approach to build Sparse Large Margin Classifiers (SLMC) by adding one more constraint to the standard Support Vector Machine (SVM) training problem. The added constraint explicitly controls the sparseness of the classifier and an approach is provided to solve the formulated problem. When considering the dual of this problem, it can be seen that building an SLMC is equivalent to constructing an SVM with a modified kernel function. Further analysis of this kernel function indicates that the proposed approach essentially finds a discriminating subspace that can be spanned by a small number of vectors, and in this subspace different classes of data are linearly well separated. Experimental results over several classification benchmarks show that in most cases the proposed approach outperforms the state-of-art sparse learning algorithms.
Author(s): | Wu, M. and Schölkopf, B. and BakIr, G. |
Book Title: | Proceedings of the 22nd International Conference on Machine Learning |
Pages: | 996-1003 |
Year: | 2005 |
Month: | August |
Day: | 0 |
Editors: | L De Raedt and S Wrobel |
Publisher: | ACM |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
DOI: | 10.1145/1102351.1102477 |
Event Name: | ICML 2005 |
Event Place: | Bonn, Germany |
Address: | New York, NY, USA |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
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BibTex @inproceedings{3385, title = {Building Sparse Large Margin Classifiers}, author = {Wu, M. and Sch{\"o}lkopf, B. and BakIr, G.}, booktitle = {Proceedings of the 22nd International Conference on Machine Learning}, pages = {996-1003}, editors = {L De Raedt and S Wrobel }, publisher = {ACM}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {New York, NY, USA}, month = aug, year = {2005}, doi = {10.1145/1102351.1102477}, month_numeric = {8} } |