An alternative to SVM Method for Data Classification

Support vector machine (SVM), is a popular kernel method for data classification that demonstrated its efficiency for a large range of practical applications. The method suffers, however, from some weaknesses including; time processing, risk of failure of the optimization process for high dimension...

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Bibliographic Details
Published in:arXiv.org 2023-08
Main Author: Remaki, Lakhdar
Format: Article
Language:English
Subjects:
Classification
Kernel functions
Optimization
Subspaces
Support vector machines
Online Access:Get full text
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Summary:Support vector machine (SVM), is a popular kernel method for data classification that demonstrated its efficiency for a large range of practical applications. The method suffers, however, from some weaknesses including; time processing, risk of failure of the optimization process for high dimension cases, generalization to multi-classes, unbalanced classes, and dynamic classification. In this paper an alternative method is proposed having a similar performance, with a sensitive improvement of the aforementioned shortcomings. The new method is based on a minimum distance to optimal subspaces containing the mapped original classes.
ISSN:2331-8422