Zhang Yanxia; Zhao Yongheng
Source Publicationchinesejournalofastronomyandastrophysics
AbstractThe sizes of astronomical surveys in different wavebands are increas-ing rapidly. Therefore, automatic classification of objects is becoming ever moreimportant. We explore the performance of learning vector quantization (LVQ) inclassifying multi-wavelength data. Our analysis concentrates on separating activesources from non-active ones. Different classes of X-ray emitters populate distinctregions of a multidimensional parameter space. In order to explore the distributionof various objects in a multidimensional parameter space, we positionally cross-correlate the data of quasars, BL Lacs, active galaxies, stars and normal galaxiesin the optical, X-ray and infrared bands. We then apply LVQ to classify them withthe obtained data. Our results show that LVQ is an effective method for separatingAGNs from stars and normal galaxies with multi-wavelength data.
Document Type期刊论文
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang Yanxia,Zhao Yongheng. learningvectorquantizationforclassifyingastronomical[J]. chinesejournalofastronomyandastrophysics,2003,3(2):183.
APA Zhang Yanxia,&Zhao Yongheng.(2003).learningvectorquantizationforclassifyingastronomical.chinesejournalofastronomyandastrophysics,3(2),183.
MLA Zhang Yanxia,et al."learningvectorquantizationforclassifyingastronomical".chinesejournalofastronomyandastrophysics 3.2(2003):183.
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