Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder-Reference-Cited by-同舟云学术

Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder

Published:2017-12 Issue:12 Volume:18 Page:1991-2000
ISSN:2095-9184
Container-title:Frontiers of Information Technology & Electronic Engineering
language:en
Short-container-title:Frontiers Inf Technol Electronic Eng

Author:

Qi Zong-feng^ORCID,Liu Qiao-qiao,Wang Jun,Li Jian-xun

Funder

National Natural Science Foundation of China

the National Basic Research Program (973) of China

Publisher

Zhejiang University Press

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing

Link

http://link.springer.com/article/10.1631/FITEE.1601395/fulltext.html

Reference22 articles.

1. Cao, S.C., Zhang, F., 2014. Review of battle damage assessment. Mil. Econ. Res., (8):53–56 (in Chinese).

2. Chen, X., Li, L., Liu, D., 2011. Battle damage level prediction on fuzzy theory and Bayesian method. IEEE Conf. on Robotics, Automation and Mechatronics, p.295–298. https://doi.org/10.1109/RAMECH.2011.6070499

3. Ding, Y., Li, N., Zhao, Y., et al., 2016. Image quality assessment method based on nonlinear feature extraction in kernel space. Front. Inform. Technol. Electron. Eng., 17(10):1008–1017. https://doi.org/10.1631/FITEE.1500439

4. Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning (2nd Ed.). Springer, New York, USA. https://doi.org/10.1007/978-0-387-84858-7

5. Hosmer, D.W., Lemeshow, S., 2005. Applied Logistic Regression (2nd Ed.). John Wiley & Sons, New York, USA. https://doi.org/10.1002/0471722146

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