@inproceedings{oai:jaxa.repo.nii.ac.jp:00003150,
 author = {Poliszczuk, Artem and Solarz, Aleksandra and Pollo, Agnieszka and The NEP-Deep Team},
 book = {宇宙航空研究開発機構特別資料, JAXA Special Publication: The Cosmic Wheel and the Legacy of the AKARI archive: from galaxies and stars to planets and life},
 month = {Mar},
 note = {第4回「あかり」国際会議 (2017年10月17-20日. 東京大学), 文京区, 東京, The 4th AKARI International Conference: The Cosmic Wheel and the Legacy of the AKARI archive: from galaxies and stars to planets and life (October 17-20, 2017. The University of Tokyo), Bunkyo-ku, Tokyo, Japan, In this proceedings application of a fuzzy Support Vector Machine (FSVM) learning algorithm, to classify mid-infrared (MIR) sources from the AKARI NEP Deep field into three classes: stars, galaxies and AGNs, is presented. FSVM is an improved version of the classical SVM algorithm, incorporating measurement errors into the classification process; this is the first successful application of this algorithm in the astronomy. We created reliable catalogues of galaxies, stars and AGNs consisting of objects with MIR measurements, some of them with no optical counterparts. Some examples of identified objects are shown, among them O-rich and C-rich AGB stars., 形態: カラー図版あり, Physical characteristics: Original contains color illustrations, 資料番号: AA1730026076, レポート番号: JAXA-SP-17-009E},
 pages = {375--378},
 publisher = {宇宙航空研究開発機構(JAXA), Japan Aerospace Exploration Agency (JAXA)},
 title = {Searching for previously unknown classes of objects in the AKARI-NEP Deep data with fuzzy logic SVM algorithm},
 volume = {JAXA-SP-17-009E},
 year = {2018}
}