Title

On Extending The Smo Algorithm Sub-Problem

Abstract

The Support Vector Machine is a widely employed machine learning model due to its repeatedly demonstrated superior generalization performance. The Sequential Minimal Optimization (SMO) algorithm is one of the most popular SVM training approaches. SMO is fast, as well as easy to implement; however, it has a limited working set size (2 points only). Faster training times can result if the working set size can be increased without significantly increasing the computational complexity. In this paper, we extend the 2-point SMO formulation to a 4-point formulation and address the theoretical issues associated with such an extension. We show that modifying the SMO algorithm to increase the working set size is beneficial in terms of the number of iterations required for convergence, and shows promise for reducing the overall training time. ©2007 IEEE.

Publication Date

12-1-2007

Publication Title

IEEE International Conference on Neural Networks - Conference Proceedings

Number of Pages

886-891

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/IJCNN.2007.4371075

Socpus ID

51749089036 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/51749089036

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