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020 ▼a 9781392579626
035 ▼a (MiAaPQ)AAI27544420
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 310
1001 ▼a Li, Cheng .
24510 ▼a Reduction Methods for Multi-Label Classification.
260 ▼a [S.l.]: ▼b Northeastern University., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 135 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
500 ▼a Advisor: Aslam, Javed.
5021 ▼a Thesis (Ph.D.)--Northeastern University, 2019.
506 ▼a This item must not be sold to any third party vendors.
520 ▼a Multi-label classification is an important machine learning task wherein one predicts a set of labels to associate with a given object. For example, an article can belong to multiple categories
590 ▼a School code: 0160.
650 4 ▼a Artificial intelligence.
650 4 ▼a Computer science.
650 4 ▼a Statistics.
690 ▼a 0800
690 ▼a 0984
690 ▼a 0463
71020 ▼a Northeastern University. ▼b Computer Science.
7730 ▼t Dissertations Abstracts International ▼g 81-06B.
773 ▼t Dissertation Abstract International
790 ▼a 0160
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15494479 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1816162
991 ▼a E-BOOK