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Please use this identifier to cite or link to this item: http://uvadoc.uva.es/handle/10324/27626
Title: Application of machine learning techniques to optical communication systems and networks
Authors: Mata Gómez, Francisco Javier
Editors: Universidad de Valladolid. Escuela Técnica Superior de Ingenieros de Telecomunicación
Tutor: Miguel Jiménez, Ignacio de
Durán Barroso, Ramón José
Issue Date: 2017
Degree : Máster en Investigación en Tecnologías de la Información y las Comunicaciones
Abstract: This TFM reviews the application of machine learning techniques in optical communication systems and networks. In addition, it studies and compares the characteristics of various machine learning methods, such as: support vector machines, logistic regression, decision trees and random forests, to predict the quality of transmission when using optical circuits in wavelength routed optical communication networks. The models developed in this TFM offer better performance than previous proposals, mainly in terms of computing time, making possible its use in online mode even in highly dynamic networks, in addition to being simpler.
Classification: Machine learning techniques
Decision trees
Support vector machines
Logistic regression
Departament : Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática
Language: eng
URI: http://uvadoc.uva.es/handle/10324/27626
Rights: info:eu-repo/semantics/openAccess
Appears in Collections:Trabajos Fin de Máster UVa

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