A Deep Learning Approach to Anomaly Detection in the Gaia Space Mission Data-Reference-Cited by-同舟云学术

A Deep Learning Approach to Anomaly Detection in the Gaia Space Mission Data

Published:2019 Issue: Volume: Page:390-401
ISSN:0302-9743
Container-title:Advances in Computational Intelligence
language:
Short-container-title:

Author:

Druetto Alessandro,Roberti Marco,Cancelliere Rossella,Cavagnino Davide,Gai Mario

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-20518-8_33

Reference34 articles.

1. Abadi, M., et al.: TensorFlow: a system for large-scale machine learning. In: Keeton, K., Roscoe, T. (eds.) 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016, Savannah, GA, USA, 2–4 November 2016, pp. 265–283. USENIX Association (2016)

2. Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Comput. 9(7), 1545–1588 (1997)

3. Lecture Notes in Computer Science;RE Banfield,2004

4. Bernard, S., Heutte, L., Adam, S.: On the selection of decision trees in Random Forests. In: 2009 International Joint Conference on Neural Networks, pp. 302–307 (2009)

5. Bharathidason, S.: Improving classification accuracy based on random forest model with uncorrelated high performing trees (2014)

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial Intelligence for Trusted Autonomous Satellite Operations;Progress in Aerospace Sciences;2024-01

2. Anomaly Detection Techniques in the Gaia Space Mission Data;Journal of Signal Processing Systems;2021-09-13

3. A method for finding anomalous astronomical light curves and their analogues;Monthly Notices of the Royal Astronomical Society;2021-09-11