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Fast Sparse Approximation for Least Squares Support Vector Machine.
- Source :
-
IEEE Transactions on Neural Networks . May2007, Vol. 18 Issue 3, p685-697. 13p. 19 Charts, 3 Graphs. - Publication Year :
- 2007
-
Abstract
- In this paper, we present two fast sparse approximation schemes for least squares support vector machine (LS-SVM), named FSALS-SVM and PFSALS-SVM, to overcome the limitation of LS-SVM that it is not applicable to large data sets and to improve test speed. FSALS-SVM iteratively builds the decision function by adding one basis function from a kernel-based dictionary at one time. The process is terminated by using a flexible and stable epsilon insensitive stopping criterion. A probabilistic speedup scheme is employed to further improve the speed of FSALS-SVM and the resulting classifier is named PFSALS-SVM. Our algorithms are of two compelling features: low complexity and sparse solution. Experiments on benchmark data sets show that our algorithms obtain sparse classifiers at a rather low cost without sacrificing the generalization performance. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10459227
- Volume :
- 18
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- 25096734
- Full Text :
- https://doi.org/10.1109/TNN.2006.889500