Research Article

On the Rényi Entropy Functional, Tsallis Distributions and Lévy Stable Distributions with Entropic Applications to Machine Learning

Authors

Ismail A Mageed * PhD, AIMMA, IEEE, IAENG, School of Computer Science, AI, and Electronics, Faculty of Engineering and Digital Technologies, University of Bradford, United Kingdom.

https://doi.org/10.22105/scfa.v1i2.33

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Abstract

The Rényi entropy function was optimized for the novel findings of this investigation under various conditions. Their research's findings suggest that generalized t-distributions cover the complete spectrum of Lévy stable distributions. Additionally, an exposition was undertaken to prove that the Lévy distribution generalizes the Tsallisian distribution rather than the reverse. The current study is a strong generalization of an existing research work in the literature. Notable entropic applications to machine learning are addressed. Concluding remarks are given, combined with some strong open problems and future research pathways.

Keywords

Shannon entropy functional Rényi entropy functional Tsallis non-extensive entropy functional Lévy stable distributions Machine learning

References (23)

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Published 2024-05-17
Issue Vol. 1 No. 2 (2024): Soft Computing Fusion with Applications
Section Articles
Document Type Research Article
Views 1130
Downloads 575

How to Cite

A Mageed, I. (2024). On the Rényi Entropy Functional, Tsallis Distributions and Lévy Stable Distributions with Entropic Applications to Machine Learning. Soft Computing Fusion With Applications , 1(2), 91-102. https://doi.org/10.22105/scfa.v1i2.33

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