Olorisade, BK, Brereton, P and Andras, PE (2022) Structural Complexity and Performance of Support Vector Machines. In: 2022 International Joint Conference on Neural Networks (IJCNN), 18-23 July 2022, Padua, Italy.

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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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: The final version of this article and all relevant information related to it, including copyrights, can be found on the publisher website.
Uncontrolled Keywords: prediction error; statistical reliability; structural complexity; support vector machine; text mining
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
T Technology > T Technology (General)
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 06 Dec 2022 12:27
Last Modified: 21 Apr 2023 07:58
URI: https://eprints.keele.ac.uk/id/eprint/11750

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