An Embedded Feature Selection Framework for Control-Reference-Cited by-同舟云学术

An Embedded Feature Selection Framework for Control

Published:2022-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wei Jiawen¹,Wang Fangyuan²,Zeng Wanxin¹,Lin Wenwei¹,Gui Ning¹

Affiliation:

1. Central South University, Changsha, China

2. Zhejiang Sci-Tech University, Hangzhou, China

Funder

National Science Foundation of China

Equipment Pre-research Key Laboratory Funds

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3534678.3539290

Reference37 articles.

1. Joshua Achiam. 2018. Spinning Up in Deep Reinforcement Learning. (2018). Joshua Achiam. 2018. Spinning Up in Deep Reinforcement Learning. (2018).

2. Martin Alnæs , Jan Blechta , Johan Hake , August Johansson , Benjamin Kehlet , Anders Logg , Chris Richardson , Johannes Ring , Marie E Rognes , and Garth N Wells . 2015. The FEniCS project version 1.5. Archive of Numerical Software 3, 100 ( 2015 ). Martin Alnæs, Jan Blechta, Johan Hake, August Johansson, Benjamin Kehlet, Anders Logg, Chris Richardson, Johannes Ring, Marie E Rognes, and Garth N Wells. 2015. The FEniCS project version 1.5. Archive of Numerical Software 3, 100 (2015).

3. Yoshua Bengio , Aaron Courville , and Pascal Vincent . 2013. Representation learning: A review and new perspectives . IEEE transactions on pattern analysis and machine intelligence 35, 8 ( 2013 ), 1798--1828. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence 35, 8 (2013), 1798--1828.

4. Leo Breiman . 2001. Random forests. Machine learning 45, 1 ( 2001 ), 5--32. Leo Breiman. 2001. Random forests. Machine learning 45, 1 (2001), 5--32.

5. Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements