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Automated clustering algorithms for classification of astronomical objects
Zhang, Y; Zhao, Y
2004-08-01
发表期刊ASTRONOMY & ASTROPHYSICS
卷号422期号:3页码:1113-1121
摘要Data mining is an important and challenging problem for the efficient analysis of large astronomical databases and will become even more important with the development of the Global Virtual Observatory. In this study, learning vector quantization (LVQ), single-layer perceptron (SLP) and support vector machines (SVM) were used for multi-wavelength data classification. A feature selection technique was used to evaluate the significance of the considered features for the results of classification. We conclude that in the situation of fewer features, LVQ and SLP show better performance. In contrast, SVM shows better performance when considering more features. The focus of the automatic classification is on the development of an efficient feature-based classifier. The classifiers trained by these methods can be used to preselect AGN candidates.
关键词Methods : Data Analysis Methods : Statistical Astronomical Data Bases : Miscellaneous Catalogs
DOI10.1051/0004-6361:20040141
收录类别SCI
语种英语
WOS记录号WOS:000223659500037
引用统计
被引频次:43[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.bao.ac.cn/handle/114a11/8113
专题应用天文研究部
作者单位Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
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GB/T 7714
Zhang, Y,Zhao, Y. Automated clustering algorithms for classification of astronomical objects[J]. ASTRONOMY & ASTROPHYSICS,2004,422(3):1113-1121.
APA Zhang, Y,&Zhao, Y.(2004).Automated clustering algorithms for classification of astronomical objects.ASTRONOMY & ASTROPHYSICS,422(3),1113-1121.
MLA Zhang, Y,et al."Automated clustering algorithms for classification of astronomical objects".ASTRONOMY & ASTROPHYSICS 422.3(2004):1113-1121.
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