秉承一贯对客户满意度的承诺，SAP日前面向全球客户宣布推出了一项全新的综合性分级支持服务模式，该创新服务模式包括SAP Enterprise Support（企业支持系列服务）以及SAP Standard Support（标准支持服务）等选项，从而使所有客户都...秉承一贯对客户满意度的承诺，SAP日前面向全球客户宣布推出了一项全新的综合性分级支持服务模式，该创新服务模式包括SAP Enterprise Support（企业支持系列服务）以及SAP Standard Support（标准支持服务）等选项，从而使所有客户都可以根据自己的需要选择最适合的支持服务类型。展开更多
We 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 ...We 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.展开更多
文摘秉承一贯对客户满意度的承诺，SAP日前面向全球客户宣布推出了一项全新的综合性分级支持服务模式，该创新服务模式包括SAP Enterprise Support（企业支持系列服务）以及SAP Standard Support（标准支持服务）等选项，从而使所有客户都可以根据自己的需要选择最适合的支持服务类型。
基金supported by the National Natural Science Foundation of China(Grant Nos.10778724,11178021 and 11033001)the Natural Science Foundation of Education Department of Hebei Province (Grant No.ZD2010127)the Young Researcher Grant of National Astronomical Observatories,Chinese Academy of Sciences
文摘We 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.
