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020 ▼a 9780438136175
035 ▼a (MiAaPQ)AAI10903792
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 020
1001 ▼a Ye, Jianbo.
24510 ▼a Computational Modeling of Compositional and Relational Data Using Optimal Transport and Probabilistic Models.
260 ▼a [S.l.] : ▼b The Pennsylvania State University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 179 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: A.
5021 ▼a Thesis (Ph.D.)--The Pennsylvania State University, 2018.
520 ▼a Quantitative researchers often view our world as a large collection of data generated and organized by the structures and functions of society and technology. Those data are usually presented and accessed with hierarchies, compositions, and rela
520 ▼a The goal of this thesis research is to introduce new mathematical models and computational methods for analyzing large-scale compositional and relational data, as well as to validate the models' usefulness in solving real-world problems. We begi
590 ▼a School code: 0176.
650 4 ▼a Information science.
650 4 ▼a Computer science.
690 ▼a 0723
690 ▼a 0984
71020 ▼a The Pennsylvania State University. ▼b Information Sciences and Technology.
7730 ▼t Dissertation Abstracts International ▼g 79-12A(E).
773 ▼t Dissertation Abstract International
790 ▼a 0176
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000749 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자