Spectroscopy and Spectral Analysis, Volume. 39, Issue 3, 948(2019)
Stellar Spectra Classification by Support Vector Machine with Unlabeled Data
[1] [1] Bazarghan M. Astrophysics and Space Science, 2012, 337(1): 93.
[2] [2] Navarro S G, Corradi R L M, Mampaso A. Astronomy and Astrophysics, 2012, 538(1): 143.
[3] [3] Bolton A S, Schlegel D J, Aubourg E, et al. The Astronomical Journal, 2012, 144(5): 507.
[4] [4] Hernandez R D, Barreto H P, Robles L A, et al. Experimental Astronomy, 2014, 38(1): 193.
[5] [5] Gray R O, Corbally C J. The Astronomical Journal, 2014, 147(4): 80.
[6] [6] Fuentes O, Gulati R K. Proceedings of the 7th Texas-Mexico Conference on Astrophysics: Flows, Blows and Glows, 2001. 209.
[8] [8] Bu Y D, Chen F Q, Pan J C. New Astronomy, 2014, 28: 35.
[9] [9] Bu Y D, Pan J C, Jiang B, et al. Publications of the Astronomical Society of Japan, 2013, 65(4): 173.
[10] [10] Cai J H, Zhao X J, Sun S W, et al. Research in Astronomy and Astrophysics, 2013, 13(3): 334.
[11] [11] Liu Z B, Song L P, Zhao W J. Monthly Notices of the Royal Astronomical Society, 2016, 455(4): 4289.
[12] [12] Liu Z B. Journal of Astrophysics and Astronomy, 2016, 37(2): 1.
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LIU Zhong-bao, LEI Yu-fei, SONG Wen-ai, ZHANG Jing, WANG Jie, TU Liang-ping. Stellar Spectra Classification by Support Vector Machine with Unlabeled Data[J]. Spectroscopy and Spectral Analysis, 2019, 39(3): 948
Received: Feb. 11, 2018
Accepted: --
Published Online: Mar. 19, 2019
The Author Email: Zhong-bao LIU (liuzb@nuc.edu.cn)