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Natural Computing

A regularization-reinforced DBN for digital recognition

  • 13-02-2017
Published in:
Authors
Junfei Qiao
Guangyuan Pan
Honggui Han
Published in
Natural Computing | Issue 4/2019

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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.

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previous article New measures for comparing optimization algorithms on dynamic optimization problems
next article A hybrid instance-intensive workflow scheduling method in private cloud environment
Title
A regularization-reinforced DBN for digital recognition
Authors
Junfei Qiao
Guangyuan Pan
Honggui Han
Publication date
13-02-2017
Publisher
Springer Netherlands
Published in
Natural Computing / Issue 4/2019
Print ISSN: 1567-7818
Electronic ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-016-9597-7
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