XGBOOST Based Stellar Spectral Classification and Quantized Feature

ZHANG Xiao; LUO A-li

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

Journals >Spectroscopy and Spectral Analysis >Volume 39 >Issue 10 >Page 3292 > Article

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

XGBOOST Based Stellar Spectral Classification and Quantized Feature

ZHANG Xiao^1、2 and LUO A-li¹

Author Affiliations

¹[in Chinese]

²[in Chinese]

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

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

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References

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

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

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

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

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

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

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

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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