Name
Abstract | ||
|---|---|---|
The problem of over fitting in DBN is extensively focused on since different networks may respond differently to an unknown input. In this study, a regularization-reinforced deep belief network (RrDBN) is proposed to improve generalization ability. In RrDBN, a special regularization-reinforced term is developed to make the weights in the unsupervised training process to attain a minimum magnitude. Then, the non-contributing weights are reduced and the resultant network can represent the inter-relations of the input–output characteristics. Therefore, the optimization process is able to obtain the minimum-magnitude weights of RrDBN. Moreover, contrastive divergence is introduced to increase RrDBN’s convergence speed. Finally, RrDBN is applied to hand-written numbers classification and water quality prediction. The results of the experiments show that RrDBN can improve the recognition performance with less recognition errors than other existing methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11047-016-9597-7 | Natural Computing |
Keywords | DocType | Volume |
Generalization, Regularization, Recognition, Deep belief net | Journal | 18 |
Issue | ISSN | Citations |
4 | 1572-9796 | 0 |
PageRank | References | Authors |
0.34 | 18 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun-Fei Qiao | 1 | 69 | 15.62 |
| Guangyuan Pan | 2 | 0 | 0.34 |
| Hong-Gui Han | 3 | 476 | 39.06 |