A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Numerical Recipes in C++: the art of scientific computing
Numerical Recipes in C++: the art of scientific computing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
Multi-class Support Vector Machine Simplification
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Arbitrary norm support vector machines
Neural Computation
Constructing sparse kernel machines using attractors
IEEE Transactions on Neural Networks
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
On-line independent support vector machines
Pattern Recognition
Classifier complexity reduction by support vector pruning in kernel matrix learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Sparse learning for support vector classification
Pattern Recognition Letters
Fast classification in incrementally growing spaces
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
A speedup method for SVM decision
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Speeding up SVM in test phase: application to radar HRRP ATR
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
The Journal of Machine Learning Research
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Hi-index | 0.00 |
In this paper we describe a new method to reduce the complexity of support vector machines by reducing the number of necessary support vectors included in their solutions. The reduction process iteratively selects two nearest support vectors belonging to the same class and replaces them by a newly constructed vector. Through the analysis of relation between vectors in the input and feature spaces, we present the construction of new vectors that requires to find the unique maximum point of a one-variable function on the interval (0, 1), not to minimize a function of many variables with local minimums in former reduced set methods. Experimental results on real life datasets show that the proposed method is effective in reducing number of support vectors and preserving machine's generalization performance.