Model selection for Discriminative Restricted Boltzmann Machines through meta-heuristic techniques

Autor(es)

Papa, Joao P. ; Rosa, Gustavo H. ; Marana, Aparecido N. ; Scheirer, Walter ; Cox, David D.

• 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