Descripción
|
|
---|---|
Deep Neural Networks (DNN) have become a powerful, and extremely popular mechanism, which has been widely used to solve problems of varied complexity, due to their ability to make models fitted to non-linear complex problems. Despite its well-known benefits, DNNs are complex learning models whose parametrisationand architecture are made usually by hand. This paper proposes a new Evolutionary Algorithm, named EvoDeep, devoted to evolve the parameters and the architecture of a DNN in order to maximise its classification accuracy, as well as maintaining a valid sequence of layers. This model is tested against a widely used dataset of handwritten digits images. The experiments performed using this dataset show that the Evolutionary Algorithm is able to select the parameters and the DNN architecture appropriately, achieving a 98.93% accuracy in the best run. | |
Internacional
|
Si |
JCR del ISI
|
Si |
Título de la revista
|
Journal of Parallel And Distributed Computing |
ISSN
|
0743-7315 |
Factor de impacto JCR
|
1,93 |
Información de impacto
|
Datos JCR del año 2016 |
Volumen
|
117 |
DOI
|
10.1016/j.jpdc.2017.09.006 |
Número de revista
|
|
Desde la página
|
180 |
Hasta la página
|
191 |
Mes
|
SIN MES |
Ranking
|
34/104 COMPUTER SCIENCE, THEORY & METHODS |