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1
artículo
Publicado 2017
Enlace
Enlace
This article presents the application of the non-parametric Random Forest method through supervised learning, as an extension of classification trees. The Random Forest algorithm arises as the grouping of several classification trees. Basically it randomly selects a number of variables with which each individual tree is constructed and predictions are made with these variables that will later be weighted through the calculation of the most voted class of these trees that were generated, to finally do the prediction by Random Forest. For the application, we worked with 3168 recorded voices, for which the results of an acoustic analysis are presented, registering variables such as frequency, spectrum, modulation, among others, seeking to obtain a pattern of identification and classification according to gender through a voice identifier. The data record used is in open access and can be do...
2
artículo
Publicado 2017
Enlace
Enlace
This article presents the application of the non-parametric Random Forest method through supervised learning, as an extension of classification trees. The Random Forest algorithm arises as the grouping of several classification trees. Basically it randomly selects a number of variables with which each individual tree is constructed and predictions are made with these variables that will later be weighted through the calculation of the most voted class of these trees that were generated, to finally do the prediction by Random Forest. For the application, we worked with 3168 recorded voices, for which the results of an acoustic analysis are presented, registering variables such as frequency, spectrum, modulation, among others, seeking to obtain a pattern of identification and classification according to gender through a voice identifier. The data record used is in open access and can be do...
3
artículo
Publicado 2017
Enlace
Enlace
This article presents the application of the non-parametric Random Forest method through supervised learning, as an extension of classification trees. The Random Forest algorithm arises as the grouping of several classification trees. Basically it randomly selects a number of variables with which each individual tree is constructed and predictions are made with these variables that will later be weighted through the calculation of the most voted class of these trees that were generated, to finally do the prediction by Random Forest. For the application, we worked with 3168 recorded voices, for which the results of an acoustic analysis are presented, registering variables such as frequency, spectrum, modulation, among others, seeking to obtain a pattern of identification and classification according to gender through a voice identifier. The data record used is in open access and can be do...
4
tesis de grado
Publicado 2022
Enlace
Enlace
Con la publicación de la Guía de diseño Mecanístico - Empírico de pavimentos (MEPDG) se ha implementado el concepto de espectros de carga que es actualmente la mejor representación de la distribución y daño de las cargas por eje y tipo vehículo que se generan hacia el pavimento, dejando de lado el cálculo tradicional de ejes equivalentes (ESAL) con su eje simple patrón de 18Kips (8.2 Ton) de la Guía de AASHTO-93. El Ministerio de Transportes (MTC) en su proceso de mejora continua de la metodología empleada para el diseño de pavimentos, debe empezar a procesar la información que se viene recopilando por las estaciones de pesaje instaladas en la Red Vial Nacional y dar un primer paso a la caracterización de la carga del tráfico vehicular mediante los espectros de carga. Debido a los recursos limitados disponibles de entradas de tráfico de Nivel 1 (específicas del sitio) ...
