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Efficient revised simplex method for SVM training.

Christopher Sentelle¹, Georgios C Anagnostopoulos, Michael Georgiopoulos

¹Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA. csentelle@cfl.rr.com

IEEE Transactions on Neural Networks

|September 9, 2011

Summary

This summary is machine-generated.

This study introduces a novel approach for training support vector machines (SVMs) using a revised simplex method, enhancing computational efficiency and simplifying implementation by avoiding singularities. The new method demonstrates competitive performance against established SVM algorithms.

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Area of Science:

Machine Learning
Computational Mathematics
Optimization Algorithms

Background:

Existing active set methods for support vector machine (SVM) training face challenges with singularities during search direction computation.
These singularities necessitate complex and less efficient algorithmic implementations to ensure convergence.

Purpose of the Study:

To present a novel SVM training algorithm that guarantees nonsingularity in search direction solving.
To develop a simpler and more computationally efficient SVM training method.

Main Methods:

Utilized the revised simplex method introduced by Rusin, which ensures nonsingularity.
Implemented an efficient search direction solving method leveraging the nonsingularity guarantee.
Compared the proposed algorithm against SVM-QP, SVMLight, and LIBSVM.

Main Results:

The revised simplex method guarantees nonsingularity, simplifying implementation and avoiding rank degeneracy issues.
The proposed algorithm demonstrates competitive performance against SVM-QP.
The algorithm is particularly effective when the proportion of nonbound support vectors is high.
Achieved competitive performance against popular SVM training tools SVMLight and LIBSVM.

Conclusions:

The revised simplex method offers a more computationally efficient and simpler approach to SVM training by avoiding singularities.
The proposed algorithm is a viable and effective alternative, especially for datasets with a large fraction of nonbound support vectors.