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Open AccessArticle

Improved Neural Networks Based on Mutual Information via Information Geometry

1
College of Computer Science, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2
State Grid Information & Telecommunication Co.,Ltd., 1401 Main Building No 2 BaiGuang Avenue, Xi Cheng District, Beijing 100031, China
*
Author to whom correspondence should be addressed.
Algorithms 2019, 12(5), 103; https://doi.org/10.3390/a12050103
Received: 18 February 2019 / Revised: 1 May 2019 / Accepted: 2 May 2019 / Published: 13 May 2019
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Abstract

This paper presents a new algorithm based on the theory of mutual information and information geometry. This algorithm places emphasis on adaptive mutual information estimation and maximum likelihood estimation. With the theory of information geometry, we adjust the mutual information along the geodesic line. Finally, we evaluate our proposal using empirical datasets that are dedicated for classification and regression. The results show that our algorithm contributes to a significant improvement over existing methods. View Full-Text
Keywords: neural networks; information geometry; geodesic line neural networks; information geometry; geodesic line
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Wang, M.; Xiao, C.-B.; Ning, Z.-H.; Yu, J.; Zhang, Y.-H.; Pang, J. Improved Neural Networks Based on Mutual Information via Information Geometry. Algorithms 2019, 12, 103.

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