Overview of Optimization Algorithms for Large-scale Support Vector Machines

Support vector machine (SVM) is one of the most classical machine learning algorithms, which performs well in many fields. However, the traditional training algorithms are not satisfactory in dealing with big data. Therefore, many algorithms for large-scale datasets have been proposed. Considering t...

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Bibliographic Details
Main Authors: Ju, Xuchan, Yan, Zhenghao, Wang, Tianhe
Format: Conference Proceeding
Language:English
Subjects:
Big Data
Classification algorithms
Conferences
large-scale datasets
Machine learning algorithms
optimization
Optimization methods
Support vector machine
Support vector machine classification
Training
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Summary:Support vector machine (SVM) is one of the most classical machine learning algorithms, which performs well in many fields. However, the traditional training algorithms are not satisfactory in dealing with big data. Therefore, many algorithms for large-scale datasets have been proposed. Considering the classification problems, this paper introduces the main SVM training algorithms for large-scale datasets including the optimization methods of binary-class problems and multi-class problems.
ISSN:2375-9259
DOI:10.1109/ICDMW53433.2021.00119