Non-supervised Learning Applied to Analysis of Topological Metrics of Optical Networks
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-319-44735-3_6
Reference24 articles.
1. Araújo DRB, Bastos-Filho CJA, Martins-Filho JF (2013) Towards using DFT to characterize complex networks. In: XXXI Simpósio Brasileiro de Telecomunicacoes (SBrT2013), pp 1–5
2. Araújo DRB, Bastos-Filho CJA, Martins-Filho JF (2014) Métricas de redes complexas para análise de redes Ópticas. Revista de Tecnologia da Informacao e Comunicacao 4(2):1–8
3. Araújo DRB, Bastos-Filho CJA, Martins-Filho JF (2014) Using the entropy of DFT of the Laplacian eigenvalues to assess networks. In: Complex networks V. Studies in computational intelligence, vol 549. Springer, Heidelberg, pp 209–216
4. Araújo DRB, Bastos-Filho CJA, Martins-Filho JF (2015) Artificial neural networks to estimate blocking probability of transparent optical networks: a robustness study for different networks. In: Proceedings of the 17th international conference on transparent optical networks ICTON 2015
5. Araújo DRB, Bastos-Filho CJA, Martins-Filho JF (2015) Methodology to obtain a fast and accurate estimator for blocking probability of optical networks. J Opt Commun Netw 7(5):380–391
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