Cite

O. A. Arqub, M. Al-Smadi and N. Shawagfeh, Solving Fredholm integro–differential equations using reproducing kernel Hilbert space method. Applied Mathematics and Computation, 2013.219 (17):pp. 8938-8948. Search in Google Scholar

T. Karvonen, C. Oates and M. Girolami, Integration in reproducing kernel Hilbert spaces of Gaussian kernels. Mathematics of Computation, 2021.90 (331):pp.2209-2233. Search in Google Scholar

N. Aronszajn, Theory of reproducing kernels. Transactions of the American Mathematical Society, 1950.68:pp.337–404. Search in Google Scholar

M. Mouattamid, Recursive Reproducing Kernels Hilbert Spaces Using the Theory of Power Kernels. Analysis in theory & applications, 2012.28 (2):pp.111–124. Search in Google Scholar

V. R. M. Elias, V. C. Gogineni, W. A. Martins and S. Werner, Kernel Regression Over Graphs Using Random Fourier Features. IEEE Transactions on Signal Processing, 2022.70:pp.936-949. Search in Google Scholar

Y. Yang, M. Pilanci, and M. J. Wainwright, Randomized sketches for kernels: Fast and optimal non-parametric regression. The Annals of Statistics, 2017.45:pp.991–1023. Search in Google Scholar

M. Li, W. Bi, J. T. Kwok and B. L. Lu, Large-Scale Nyström Kernel Matrix Approximation Using Randomized SVD. IEEE Transaction on Neural Networks and Learning Systems, 2015.26 (1): pp.152-164. Search in Google Scholar

M. Xu, J. Niu and Y. Lin, An efficient method for fractional nonlinear differential equations by quasi‐Newton’s method and simplified reproducing kernel method. Mathematical Methods in the Applied Sciences, 2018.41 (1):pp.5-14. Search in Google Scholar

S. Kumar, M. Mohri, and A. Talwalkar, Sampling methods for the nyström method. The Journal of Machine Learning Research, 2012.13 (1):pp.981–1006. Search in Google Scholar

J. Wu, L. Ding and S. Liao, Predictive Nyström method for kernel methods. Neurocomputing (Amsterdam), 2017.234:pp.116-125. Search in Google Scholar

G. B. Huang, An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels. Cognitive Computation, 2014.6 (3):pp.376-390. Search in Google Scholar

D. P. Francis and K. Raimond, Major advancements in kernel function approximation. The Artificial Intelligence Review, 2020.54 (2):pp.843-876. Search in Google Scholar

K. A. Touchent, Z. Hammouch and T. Mekkaoui, A modified invariant subspace method for solving partial differential equations with non-singular kernel fractional derivatives. Applied Mathematics and Nonlinear Sciences, 2020.5 (2):pp.35-48. Search in Google Scholar

M. Aledhari, R. Razzak and R. M. Parizi, Machine learning for network application security: Empirical evaluation and optimization. Computers & Electrical Engineering, 2021.91:pp.107052. Search in Google Scholar

A. A. Nunes, M. Mendonca, X. N. Nguyen, K. Obraczka and T. Turletti, A Survey of Software-Defined Networking: Past, Present, and Future of Programmable Networks. IEEE Communications Surveys and Tutorials, 2014.16 (3):pp.1617-1634. Search in Google Scholar

J. Wu, J. Yuan and W. Gao, Analysis of fractional factor system for data transmission in SDN. Applied Mathematics and Nonlinear Sciences, 2019.4 (1):pp.191-196. Search in Google Scholar

F. Macedo, D. Guedes, L. F. M. Vieira, M. A. M. Vieira and M. Nogueira, Programmable Networks-From Software-Defined Radio to Software-Defined Networking. IEEE Communications Surveys and Tutorials, 2015.17 (2):pp.1102-1125. Search in Google Scholar

S. W. Smith and S. Weingart, Building a high-performance, programmable secure coprocessor. Computer Networks (Amsterdam, Netherlands : 1999), 1999.31 (8):pp.831-860. Search in Google Scholar

L. Koc, T. Mazzuchi and S. Sarkani, A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier. Expert Systems with Applications, 2012.39 (18):pp.13492-13500. Search in Google Scholar

D. Kreutz, R. Diego, M. V. Fernando, P. E. Verissimo, C. E. Rothenberg, S. Azodolmolky, and S. Uhlig, Software-Defined Networking: A Comprehensive Survey. Proceedings of the IEEE, 2015.103 (1):pp.14-76. Search in Google Scholar

S. Wang, J. Balarezo, S. Kandeepan, A. Al-Hourani, K. Gomez and B. Rubinstein, (2021). Machine Learning in Network Anomaly Detection: A Survey. IEEE Access, 9: 1. Search in Google Scholar

S. Muller, J. Lancrenon, C. Harpes, Carlo, Y. Le Traon, S. Gombault and J. Bonnin, (2018), A training-resistant anomaly detection system. Computers & Security, 76:1-11. Search in Google Scholar

B. Wei, L. Wang, Lin, J. Zhu, M. Zhang, L. Xing and Q. Wu, Flow control oriented forwarding and caching in cache-enabled networks. Journal of Network and Computer Applications, 2021.196: pp.103248. Search in Google Scholar

P. Bradley, The use of the area under the roc curve in the evaluation of machine learning algorithms, Pattern Recognition, 1997.30 (7):pp.1145-1159. Search in Google Scholar

J. Hand, Measuring classifier performance: a coherent alternative to the area under the roc curve, Machine Learning, 2009.77 (1):pp.103-123. Search in Google Scholar

eISSN:
2444-8656
Idioma:
Inglés
Calendario de la edición:
Volume Open
Temas de la revista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics