On the Rényi Entropy Functional, Tsallis Distributions and Lévy Stable Distributions with Entropic Applications to Machine Learning
DOI:
https://doi.org/10.22105/cz6btf75Keywords:
Shannon entropy functional, Rényi entropy functional, Tsallis non-extensive entropy functional, Lévy stable distributions, Machine learningAbstract
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.
References
Mageed, I. A. (2023). The consistency axioms of the stable M/G/1 queue’s z a, b non-extensive maximum entropy formalism with M/G/1 theory applications to 6G networks and multimedia applications. 2023 international conference on computer and applications (ICCA) (pp. 1–6). IEEE. DOI: 10.1109/ICCA59364.2023.10401411
Mageed, I. A., Zhang, Q., Kouvatsos, D. D., & Shah, N. (2022). M/G/1 queue with balking shannonian maximum entropy closed form expression with some potential queueing applications to energy. IEEE global energy conference, GEC 2022 (pp. 105–110). IEEE. DOI: 10.1109/GEC55014.2022.9987144
Mageed, I. A., & Zhang, Q. (2022). Inductive inferences of z-entropy formalism (ZEF) stable M/G/1 queue with heavy tails. 2022 27th international conference on automation and computing (ICAC) (pp. 1–6). IEEE. DOI: 10.1109/ICAC55051.2022.9911090
Mageed, I. A., & FRSS, I. (2023). Where the mighty trio meet: information theory (IT), pathway model theory (PMT) and queueing theory (QT) [presentation]. 39th annual uk performance engineering workshop.
Mageed, I. A. (2023). The entropian threshold theorems for the steady state probabilities of the stable M/G/1 Queue with Heavy Tails with Applications of Probability Density Functions to 6G networks. Electronic journal of computer science and information technology, 9(1), 24–30. https://doi.org/10.52650/ejcsit.v9i1.138
Begum, A., & Choudhury, G. (2023). Analysis of an M/(G1G2)/1 queue with bernoulli vacation and server breakdown. International journal of applied and computational mathematics, 9(1), 9. DOI: 10.1007/s40819-022-01481-4
Kloska, S., Pałczyński, K., Marciniak, T., Talaśka, T., Nitz, M., Wysocki, B. J., Davis, P., & Wysocki, T. A. (2021). Queueing theory model of Krebs cycle. Bioinformatics, 37(18), 2912–2919. DOI: 10.1093/bioinformatics/btab177
Mollaei, S., Darooneh, A. H., & Karimi, S. (2019). Multi-scale entropy analysis and Hurst exponent. Physica a: statistical mechanics and its applications, 528, 121292. DOI: 10.1016/j.physa.2019.121292
Kuaban, G. S., Soodan, B. S., Kumar, R., & Czekalski, P. (2022). A queueing-theoretic analysis of the performance of a cloud computing infrastructure: accounting for task reneging or dropping. 2022 international conference on electrical, computer, communications and mechatronics engineering (ICECCME) (pp. 1–7). IEEE. DOI: 10.1109/ICECCME55909.2022.9988250
Kłopotek, M. A., & Kłopotek, R. A. (2023). Towards continuous consistency axiom. Applied intelligence, 53(5), 5635–5663. DOI: 10.1007/s10489-022-03710-1
Guo, C., Fang, S., & He, Y. (2023). A generalized stochastic process: fractional G-Brownian motion. Methodology and computing in applied probability, 25(1), 22. https://doi.org/10.1007/s11009-023-10010-9
Scully, Z., & Harchol-Balter, M. (2021). The gittins policy in the M/G/1 queue. 2021 19th international symposium on modeling and optimization in mobile, ad hoc, and wireless networks, wiopt 2021 (pp. 1–8). IEEE. DOI: 10.23919/WiOpt52861.2021.9589051
Jessup II, M. A., & others. (2012). Using hybrid simulation/analytical queueing networks to capacitate usaf air mobility command passenger terminals. https://scholar.afit.edu/etd/1214
Nielsen, F. (2020). An elementary introduction to information geometry. Entropy, 22(10), 1100. https://doi.org/10.3390/e22101100
Ouadfeul, S. A. (2024). Fractal analysis-applications and updates. BoD-books on demand.
Palamidessi, C., & Romanelli, M. (2020). Feature selection in machine learning: R’enyi min-entropy vs Shannon entropy. ArXiv preprint arxiv:2001.09654. https://doi.org/10.48550/arXiv.2001.09654
Palamidessi, C., & Romanelli, M. (2018). Feature selection with rényi min-entropy. Artificial neural networks in pattern recognition: 8th IAPR TC3 workshop, ANNPR 2018, Siena, Italy, September 19-21, 2018, proceedings 8 (pp. 226–239). Springer, Cham. https://doi.org/10.1007/978-3-319-99978-4_18
Will-Cole, A. R., Kusne, A. G., Tonner, P., Dong, C., Liang, X., Chen, H., & Sun, N. X. (2022). Application of Bayesian optimization and regression analysis to ferromagnetic materials development. IEEE transactions on magnetics, 58(1), 1–8. DOI: 10.1109/TMAG.2021.3125250
Yuan, M., Pun, M. O., & Wang, D. (2023). Rényi state entropy maximization for exploration acceleration in reinforcement learning. IEEE transactions on artificial intelligence, 4(5), 1154–1164. DOI:10.1109/TAI.2022.3185180
Borelli, R., Dovier, A., & Fogolari, F. (2022). Data structures and algorithms for k-th nearest neighbours conformational entropy estimation. Biophysica, 2(4), 340–352. DOI: 10.3390/biophysica2040031
Lim, H.-D., & Lee, D. (2022). Regularized Q-learning. ArXiv Preprint ArXiv:2202.05404. https://doi.org/10.48550/arXiv.2202.05404
Rathie, P. N., & Da Silva, S. (2008). Shannon, Lévy, and Tsallis: A note. Applied mathematical sciences, 2(28), 1359–1363.
Mageed, I. A., & Zhang, Q. (2022). An information theoretic unified global theory for a stable M/G/1 queue with potential maximum entropy applications to energy works. 2022 global energy conference (GEC) (pp. 300–305). IEEE. DOI: 10.1109/GEC55014.2022.9986719