Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6874595 | Journal of Computational Science | 2015 | 5 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
João P. Papa, Gustavo H. Rosa, Aparecido N. Marana, Walter Scheirer, David D. Cox,