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Structural Complexity and Performance of Support Vector Machines

Olorisade, Babatunde Kazeem; Brereton, Pearl; Andras, Peter


Babatunde Kazeem Olorisade

Pearl Brereton

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Prof Peter Andras
Dean of School of Computing Engineering and the Built Environment


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.

Presentation Conference Type Conference Paper (Published)
Conference Name 2022 International Joint Conference on Neural Networks (IJCNN)
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)