Efficient Parallel Optimization of Multiclass SVM using 1-Factorization Approach

Vijayakumar H. Bhajantri, Shashikumar G. Totad , Geeta R. Bharamagoudar

Efficient Parallel Optimization of Multiclass SVM using 1-Factorization Approach

Authors

Vijayakumar H. Bhajantri, Shashikumar G. Totad , Geeta R. Bharamagoudar

Keywords:

Machine Learning, Big Data, ADMM, Symmetric ADMM, Support Vector Machine, Classification, Parallel and distributed computing, Crammer-Singer, Weston-Watkins, Multiclass.

Abstract

Support Vector Machines (SVMs) are widely used for multiclass classification tasks due to theirstrong generalization capabilities. However, their computational complexity increasessignificantly with large datasets, making parallel optimization essential. This paper presents anefficient parallel optimization approach for multiclass S

References

. Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization andstatistical learning via the alternating direction method of multipliers. Foundations and Trends®in Machine Learning, 3(1), 1-122.

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Published

2024-12-29

How to Cite

Vijayakumar H. Bhajantri, Shashikumar G. Totad , Geeta R. Bharamagoudar. (2024). Efficient Parallel Optimization of Multiclass SVM using 1-Factorization Approach. Journal of Computational Analysis and Applications (JoCAAA), 33(08), 4666–4684. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2959