NAOC Open IR
基于熵学习机的恒星光谱分类
Liu Zhongbao1; Ren Juanjuan2; Song Wenai1; Zhang Jing1; Kong Xiao2; Fu Lizhen1
2018
Source Publication光谱学与光谱分析
ISSN1000-0593
Volume38.0Issue:002Pages:660
Abstract数据挖掘被广泛应用于恒星光谱分类。为了提高传统光谱分类方法性能,提出熵学习机(Entropybased Learning Machine,ELM)。在该方法中,熵用来刻画分类的不确定性。为了得到理想的分类结果,分类的不确定性应最小,基于此,可得ELM的最优化问题。ELM在处理二分类问题和稀有光谱发现等方面具有一定优势。SDSS中K型、F型、G型恒星光谱数据集上的比较实验表明:ELM在进行恒星光谱分类时,其分类性能优于k近邻(k Nearest Neighbor)和支持向量机(Support Vector Machine)等传统分类方法。
Keyword数据挖掘 恒星光谱分类 斯隆数字巡天
Language英语
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/63183
Collection中国科学院国家天文台
Affiliation1.中北大学
2.中国科学院国家天文台
Recommended Citation
GB/T 7714
Liu Zhongbao,Ren Juanjuan,Song Wenai,等. 基于熵学习机的恒星光谱分类[J]. 光谱学与光谱分析,2018,38.0(002):660.
APA Liu Zhongbao,Ren Juanjuan,Song Wenai,Zhang Jing,Kong Xiao,&Fu Lizhen.(2018).基于熵学习机的恒星光谱分类.光谱学与光谱分析,38.0(002),660.
MLA Liu Zhongbao,et al."基于熵学习机的恒星光谱分类".光谱学与光谱分析 38.0.002(2018):660.
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