Spectroscopy and Spectral Analysis, Volume. 32, Issue 2, 510(2012)

Data Mining Approach to Cataclysmic Variables Candidates Based on Random Forest Algorithm

JIANG Bin1...2,3,*, LUO A-li1 and ZHAO Yong-heng1 |Show fewer author(s)
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  • 1[in Chinese]
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    References(11)

    [1] [1] Ronald A. Downes. A Catalog and Atlas of Cataclysmic Variables: The Final Edition. The Journal of Astronomical Data, 2005: 11, 2.

    [2] [2] Szkody P, et al. The Astronomical Journal, 2002, 123: 430.

    [3] [3] Szkody P, et al. The Astronomical Journal, 2003, 126: 1499.

    [4] [4] Szkody P, et al. The Astronomical Journal, 2004, 128: 1882.

    [5] [5] Szkody P, et al. The Astronomical Journal, 2005, 129: 2386.

    [6] [6] Szkody P, et al. The Astronomical Journal, 2006, 131: 973.

    [7] [7] Szkody P, et al. The Astronomical Journal, 2007, 134: 185.

    [8] [8] Patrick Wils. Monthly Notices of the Royal Astronomical Society, V402, Issue 1, 436.

    [10] [10] Frederick Livingston. Implementation of Breiman’s Random Forest Machine Learning Algorithm ECE591Q Machine Learning Journal Paper. Fall 2005.

    [11] [11] TU Liang-ping. Research in Astronomy and Astrophysics, 2009, 9(6): 635.

    [12] [12] Abazajian K. The Astrophysical Journal Supplement, 2009, 182(2): 543.

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    JIANG Bin, LUO A-li, ZHAO Yong-heng. Data Mining Approach to Cataclysmic Variables Candidates Based on Random Forest Algorithm[J]. Spectroscopy and Spectral Analysis, 2012, 32(2): 510

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    Paper Information

    Received: Mar. 10, 2011

    Accepted: --

    Published Online: Feb. 20, 2012

    The Author Email: Bin JIANG (jiangbin@sdu.edu.cn)

    DOI:10.3964/j.issn.1000-0593(2012)02-0510-04

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