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Smart Intelligent Computing and Applications, Volume 1
Mariyam Ashai, Rhea Gautam Mukherjee, Sanjana P. Mundharikar, Vinayak Dev Kuanr and R. Harikrishnan Smart Innovation, Systems and Technologies, Smart Intelligent Computing and Applications, Volume 1 282 377 (2022) https://doi.org/10.1007/978-981-16-9669-5_34
Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky
Arodh Lal Karn, Carlos Andres Tavera Romero, Sudhakar Sengan, Abolfazl Mehbodniya, Julian L. Webber, Denis A. Pustokhin and Frank-Detlef Wende IEEE Access 10 101276 (2022) https://doi.org/10.1109/ACCESS.2022.3207480
Active galactic nuclei catalog from the AKARI NEP-Wide field
Identification of BASS DR3 sources as stars, galaxies, and quasars by XGBoost
Changhua Li, Yanxia Zhang, Chenzhou Cui, et al. Monthly Notices of the Royal Astronomical Society 506(2) 1651 (2021) https://doi.org/10.1093/mnras/stab1650
Identification of emission-line stars in transition phase from pre-main sequence to main sequence
Suman Bhattacharyya, Blesson Mathew, Gourav Banerjee, et al. Monthly Notices of the Royal Astronomical Society 507(3) 3660 (2021) https://doi.org/10.1093/mnras/stab2385
A Systematic Study of Superluminous Supernova Light-curve Models Using Clustering
Efficient selection of quasar candidates based on optical and infrared photometric data using machine learning
Dongwei Fan, Xue-bing Wu, Yongheng Zhao, et al. Monthly Notices of the Royal Astronomical Society 485(4) 4539 (2019) https://doi.org/10.1093/mnras/stz680
Probabilistic Random Forest: A Machine Learning Algorithm for Noisy Data Sets
Randomized apertures: high resolution imaging in far field
Xiaopeng Peng, Garreth J. Ruane, Marco B. Quadrelli and Grover A. Swartzlander Optics Express 25(15) 18296 (2017) https://doi.org/10.1364/OE.25.018296
Eduardo Machado, Marcello Serqueira, Eduardo Ogasawara, Ricardo Ogando, Marcio A. G. Maia, Luiz Nicolaci da Costa, Riccardo Campisano, Gustavo Paiva Guedes and Eduardo Bezerra 123 (2016) https://doi.org/10.1109/IJCNN.2016.7727189
OF GENES AND MACHINES: APPLICATION OF A COMBINATION OF MACHINE LEARNING TOOLS TO ASTRONOMY DATA SETS
S. Heinis, S. Kumar, S. Gezari, W. S. Burgett, K. C. Chambers, P. W. Draper, H. Flewelling, N. Kaiser, E. A. Magnier, N. Metcalfe and C. Waters The Astrophysical Journal 821(2) 86 (2016) https://doi.org/10.3847/0004-637X/821/2/86
Clustering of the AKARI NEP deep field 24μm selected galaxies
A support vector machine for spectral classification of emission-line galaxies from the Sloan Digital Sky Survey
Fei Shi, Yu-Yan Liu, Guang-Lan Sun, et al. Monthly Notices of the Royal Astronomical Society 453(1) 122 (2015) https://doi.org/10.1093/mnras/stv1617
Spectral Classification Using Restricted Boltzmann Machine
Chen Fuqiang, Wu Yan, Bu Yude and Zhao Guodong Publications of the Astronomical Society of Australia 31 (2014) https://doi.org/10.1017/pasa.2013.38
Stellar spectra association rule mining method based on the weighted frequent pattern tree
Jiang-Hui Cai, Xu-Jun Zhao, Shi-Wei Sun, Ji-Fu Zhang and Hai-Feng Yang Research in Astronomy and Astrophysics 13(3) 334 (2013) https://doi.org/10.1088/1674-4527/13/3/008
QUASI-STELLAR OBJECT SELECTION ALGORITHM USING TIME VARIABILITY AND MACHINE LEARNING: SELECTION OF 1620 QUASI-STELLAR OBJECT CANDIDATES FROM MACHO LARGE MAGELLANIC CLOUD DATABASE
Miguel Á. Montero, Roberto Ruíz, Miguel García-Torres and Luis M. Sarro Lecture Notes in Computer Science, Trends in Applied Intelligent Systems 6096 611 (2010) https://doi.org/10.1007/978-3-642-13022-9_61
Automated spectral classification using template matching