Spectroscopy and Spectral Analysis, Volume. 32, Issue 2, 510(2012)
Data Mining Approach to Cataclysmic Variables Candidates Based on Random Forest Algorithm
[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
Received: Mar. 10, 2011
Accepted: --
Published Online: Feb. 20, 2012
The Author Email: Bin JIANG (jiangbin@sdu.edu.cn)