期刊
APPLIED SOFT COMPUTING
卷 46, 期 -, 页码 875-885出版社
ELSEVIER
DOI: 10.1016/j.asoc.2015.08.043
关键词
Restricted Boltzmann Machines; Deep Belief Networks; Harmony Search; Meta-heuristics
资金
- FAPESP [2013/20387-7, 2014/16250-9]
- CNPq [303182/2011-3, 470571/2013-6, 306166/2014-3]
In this paper, we deal with the problem of Deep Belief Networks (DBNs) parameters fine-tuning by means of a fast meta-heuristic approach named Harmony Search (HS). Although such deep learning-based technique has been widely used in the last years, more detailed studies about how to set its parameters may not be observed in the literature. We have shown we can obtain more accurate results comparing HS against with several of its variants, a random search and two variants of the well-known Hyperopt library. The experimental results were carried out in two public datasets considering the task of binary image reconstruction, three DBN learning algorithms and three layers. (C) 2015 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据