Babatunde Kazeem Olorisade
Structural Complexity and Performance of Support Vector Machines
Olorisade, Babatunde Kazeem; Brereton, Pearl; Andras, Peter
Authors
Pearl Brereton
Prof Peter Andras P.Andras@napier.ac.uk
Dean of School of Computing Engineering and the Built Environment
Abstract
Support vector machines (SVM) are often applied in the context of machine learning analysis of various data. Given the nature of SVMs, these operate always in the sub-interpolation range as a machine learning method. Here we explore the impact of structural complexity on the performance and statistical reliability of SVMs applied for text mining. We set a theoretical framework for our analysis. We found experimentally that the statistical reliability and performance reduce exponentially with the increase of the structural complexity of the SVMs. This is an important result for the understanding of how the prediction error of SVM predictive data models behaves.
Citation
Olorisade, B. K., Brereton, P., & Andras, P. (2022). Structural Complexity and Performance of Support Vector Machines. In 2022 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/ijcnn55064.2022.9892368
Conference Name | 2022 International Joint Conference on Neural Networks (IJCNN) |
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Conference Location | Padua, Italy |
Start Date | Jul 18, 2022 |
End Date | Jul 23, 2022 |
Online Publication Date | Sep 30, 2022 |
Publication Date | 2022 |
Deposit Date | Jan 12, 2023 |
Publisher | Institute of Electrical and Electronics Engineers |
Series ISSN | 2161-4407 |
Book Title | 2022 International Joint Conference on Neural Networks (IJCNN) |
DOI | https://doi.org/10.1109/ijcnn55064.2022.9892368 |
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