| Texto completo | |
| Autor(es): |
Papa, Joao P.
;
Rosa, Gustavo H.
;
Marana, Aparecido N.
;
Scheirer, Walter
;
Cox, David D.
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | JOURNAL OF COMPUTATIONAL SCIENCE; v. 9, p. 5-pg., 2015-07-01. |
| Resumo | |
Discriminative learning of Restricted Boltzmann Machines has been recently introduced as an alternative to provide a self-contained approach for both unsupervised feature learning and classification purposes. However, one of the main problems faced by researchers interested in such approach concerns with a proper selection of its parameters, which play an important role in its final performance. In this paper, we introduced some meta-heuristic techniques for this purpose, as well as we showed they can be more accurate than a random search, which is commonly used technique in several works. (C) 2015 Elsevier B.V. All rights reserved. (AU) | |
| Processo FAPESP: | 14/16250-9 - Sobre a otimização de parâmetros em técnicas de aprendizado de máquina: avanços e paradigmas |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 13/20387-7 - Otimização de hiperparâmetros em arquiteturas de aprendizado em profundidade |
| Beneficiário: | João Paulo Papa |
| Modalidade de apoio: | Bolsas no Exterior - Pesquisa |