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Pruning support vector machines without altering performances.

Xun Liang¹, Rong-Chang Chen, Xinyu Guo

¹Institute of Computer Science and Technology, Peking University, Beijing, China. liangxun@pku.edu.cn

IEEE Transactions on Neural Networks

|October 10, 2008

Summary

This summary is machine-generated.

This study introduces a novel method to prune redundant Support Vector Machines (SVMs) by analyzing kernel output overlap. The technique effectively reduces unnecessary Support Vectors without impacting SVM performance.

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Area of Science:

Machine Learning
Computational Science

Background:

Support Vector Machines (SVMs) offer advantages like robustness and a strong theoretical basis.
Efficient training algorithms, such as Sequential Minimal Optimization (SMO), can generate a large number of Support Vectors (SVs).
Redundancy among SVs is suspected due to similar kernel output levels.

Purpose of the Study:

To develop a method for pruning dispensable Support Vectors (SVs) in SVMs.
To analyze overlapped information in kernel outputs to identify redundant SVs.
To avoid explicit SV identification in feature space during pruning.

Main Methods:

A novel method based on crosswise propagation (CP) is developed to prune dispensable SVs.
The method analyzes overlapped information of kernel outputs.
Experiments were conducted using the LibSVM software with typical kernels and various parameters.

Main Results:

The crosswise propagation (CP) method successfully identifies and prunes dispensable Support Vectors (SVs).
Experiments showed that 1% to 9% (and sometimes over 50%) of SVs were dispensable across various datasets and kernels.
The pruning method did not negatively affect the performance of the SVM models.

Conclusions:

A systematic method for pruning redundant Support Vectors (SVs) in SVMs has been developed.
The proposed method is effective in reducing SV count without compromising model performance.
A new theoretical lower upper bound on the number of SVs in high-dimensional feature spaces is presented.