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Structural Complexity and Performance of Support Vector Machines

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

(2022, July). Structural Complexity and Performance of Support Vector Machines. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy

Conference Name 2022 International Joint Conference on Neural Networks (IJCNN)
Conference Location Padua, Italy
Start Date Jul 18, 2022
End Date Jul 23, 2022
Acceptance Date Jul 18, 2022
Publication Date Jul 18, 2022
Series Title 2022 International Joint Conference on Neural Networks (IJCNN)
Keywords prediction error; statistical reliability; structural complexity; support vector machine; text mining
Publisher URL https://ieeexplore.ieee.org/document/9892368

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