| Full text | |
| Author(s): |
Papa, Joao P.
;
Rosa, Gustavo H.
;
Marana, Aparecido N.
;
Scheirer, Walter
;
Cox, David D.
Total Authors: 5
|
| Document type: | Journal article |
| Source: | JOURNAL OF COMPUTATIONAL SCIENCE; v. 9, p. 5-pg., 2015-07-01. |
| 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. (C) 2015 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/20387-7 - Hyperparameter optimization in deep learning arquitectures |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Scholarships abroad - Research |