article · 2024
An improved parallel optimization algorithm for cascade support vector machines
Cited by 0Abstract and details only
Abstract
Support Vector Machines (SVMs) are widely used machine learning algorithms, recognized for their strong performance across various classification tasks. However, their computational complexity, particularly with large datasets, poses significant challenges. Parallel learning offer partial relief, with the Cascade SVM being a prominent approach that partitions data into smaller subsets to manage computational load. Despite its advantages, the current Cascade SVM algorithm suffers from accuracy loss and inefficient convergence. In this paper, we present an improved parallel optimization algorithm for the Cascade SVM. Specifically, we design a novel exchange system between support vectors (SVs) and non-SVs, combining with screening rules to ensure the preservation of all global SVs, which significantly improves classification accuracy and accelerates convergence. Experimental results on synthetic and real-world datasets demonstrate that the proposed algorithm achieves accuracy comparable to that of a standard SVM, while offering substantial speed improvements over the traditional multi-pass Cascade SVM, particularly in large-scale and high-dimensional settings. Our method offers a robust and efficient solution for large-scale SVM training.
Cite this paper
Luo, K., & Fan, Y. (2024). An improved parallel optimization algorithm for cascade support vector machines. https://doi.org/10.1109/iscid63852.2024.00054
Luo, Keyang, and Yiwei Fan. “An improved parallel optimization algorithm for cascade support vector machines.” 2024, pp. 207–12, https://doi.org/10.1109/iscid63852.2024.00054.
Luo, Keyang, and Yiwei Fan. 2024. “An improved parallel optimization algorithm for cascade support vector machines.” https://doi.org/10.1109/iscid63852.2024.00054.
Luo, K. and Fan, Y. (2024) ‘An improved parallel optimization algorithm for cascade support vector machines’. Available at: https://doi.org/10.1109/iscid63852.2024.00054.
@article{luo2024improved,
title = {{An improved parallel optimization algorithm for cascade support vector machines}},
author = {Luo, Keyang and Fan, Yiwei},
pages = {207--212},
year = {2024},
doi = {10.1109/iscid63852.2024.00054}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.