Model selection for discriminative restricted boltzmann machines through meta-heuristic techniques

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Processo FAPESP: 2013/20387-7

Processo FAPESP: 2014/16250-9

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.