Morphological Classification of Extragalactic Radio Sources Using Gradient Boosting Methods
Author:
Affiliation:
1. SAASST, University of Sharjah,Sharjah,UAE
2. Pontifical Catholic University of Rio de Janeiro,Dept. of Electrical Engineering,Rio de Janeiro,Brazil
3. University of Sharjah,Dept. of Electrical Engineering,Sharjah,UAE
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10190990/10190992/10191163.pdf?arnumber=10191163
Reference29 articles.
1. CNN architecture comparison for radio galaxy classification
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3. Data-efficient classification of radio galaxies;samudre;Monthly Notices of the Royal Astronomical Society,2022
4. Morphological classification of compact and extended radio galaxies using convo-lutional neural networks and data augmentation techniques;maslej-kreš?aková;Monthly Notices of the Royal Astronomical Society,2021
5. Radio galaxy zoo: Claran-a deep learning classifier for radio morphologies;wu;Monthly Notices of the Royal Astronomical Society,2019
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