Header logo is ei

Building Sparse Large Margin Classifiers


Conference Paper


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

Links: PDF


  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},
  month_numeric = {8}