NAOC Open IR
asvmknnmethodforquasarstarclassification
Peng Nanbo; Zhang Yanxia; Zhao Yongheng
2013
Source Publicationsciencechinaphysicsmechanicsastronomy
ISSN1674-7348
Volume56Issue:6Pages:1227
AbstractWe integrate k-Nearest Neighbors(kNN) into Support Vector Machine(SVM) and create a new method called SVM-kNN.SVM-kNN strengthens the generalization ability of SVM and apply kNN to correct some forecast errors of SVM and improve the forecast accuracy.In addition,it can give the prediction probability of any quasar candidate through counting the nearest neighbors of that candidate which is produced by kNN.Applying photometric data of stars and quasars with spectral classification from SDSS DR7 and considering limiting magnitude error is less than 0.1,SVM-kNN and SVM reach much higher performance that all the classification metrics of quasar selection are above 97.0%.Apparently,the performance of SVM-kNN has slighter improvement than that of SVM.Therefore SVM-kNN is such a competitive and promising approach that can be used to construct the targeting catalogue of quasar candidates for large sky surveys.
Language英语
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/51345
Collection中国科学院国家天文台
Affiliation中国科学院国家天文台
First Author AffilicationNational Astronomical Observatories, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Peng Nanbo,Zhang Yanxia,Zhao Yongheng. asvmknnmethodforquasarstarclassification[J]. sciencechinaphysicsmechanicsastronomy,2013,56(6):1227.
APA Peng Nanbo,Zhang Yanxia,&Zhao Yongheng.(2013).asvmknnmethodforquasarstarclassification.sciencechinaphysicsmechanicsastronomy,56(6),1227.
MLA Peng Nanbo,et al."asvmknnmethodforquasarstarclassification".sciencechinaphysicsmechanicsastronomy 56.6(2013):1227.
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