Papa, João Paulo [UNESP]Rosa, Gustavo Henrique de [UNESP]Marana, Aparecido Nilceu [UNESP]Scheirer, WalterCox, David Daniel2016-03-022016-03-022015Journal of Computational Science, v. 1, p. 1, 2015.1877-7503http://hdl.handle.net/11449/135791Discriminative 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.14-18engDiscriminative restricted boltzmann machinesModel selectionDeep learningArtigo10.1016/j.jocs.2015.04.014Acesso restrito60277137509426899039182932747194