Fine-tuning Deep Belief Networks using Harmony Search
Carregando...
Fonte externa
Fonte externa
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Tipo
Artigo
Direito de acesso
Acesso aberto
Fonte externa
Fonte externa
Resumo
In this paper, we deal with the problem of Deep Belief Networks (DBNs) parameters fine-tuning by means of a fast meta-heuristic approach named Harmony Search (HS). Although such deep learning-based technique has been widely used in the last years, more detailed studies about how to set its parameters may not be observed in the literature. We have shown we can obtain more accurate results comparing HS against with several of its variants, a random search and two variants of the well-known Hyperopt library. The experimental results were carried out in two public datasets considering the task of binary image reconstruction, three DBN learning algorithms and three layers.
Descrição
Palavras-chave
Deep Belief Networks, Harmony Search, Meta-heuristics, Restricted Boltzmann Machines
Idioma
Inglês
Citação
Applied Soft Computing Journal, v. 46, p. 875-885.
