Spectroscopy and Spectral Analysis, Volume. 39, Issue 10, 3292(2019)

XGBOOST Based Stellar Spectral Classification and Quantized Feature

ZHANG Xiao1,2 and LUO A-li1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    References(8)

    [1] [1] Luo Ali, Zhao Yongheng, et al. Research in Astronomy and Astrophysics, 2015, 15(8): 1095.

    [2] [2] Schierscher F, Paunzen E. Astronomische Nachrichten, 2011, 332(6): 597.

    [3] [3] Hampton E J, Medling A M, Groves B, et al. Monthly Notices of the Royal Astronomical Society, 2017, 470: 3395.

    [4] [4] Liu Chao, Cui Wenyuan, et al. Research in Astronomy and Astrophysics, 2015, 15(8): 1137.

    [5] [5] Du Changde, Luo A, Yang H. New Astronomy, 2017, 51: 51.

    [6] [6] Worthey G, Faber S M, Gonzalez J Jesus, et al. The Astrophysical Journal Supplement Series, 94(2): 687.

    [8] [8] Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016. 785.

    [9] [9] Gray R O, Corbally C J. Stellar Spectral Classification. Princeton University Press, 2009. 160.

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    ZHANG Xiao, LUO A-li. XGBOOST Based Stellar Spectral Classification and Quantized Feature[J]. Spectroscopy and Spectral Analysis, 2019, 39(10): 3292

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    Paper Information

    Received: Sep. 7, 2018

    Accepted: --

    Published Online: Nov. 5, 2019

    The Author Email:

    DOI:10.3964/j.issn.1000-0593(2019)10-3292-05

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