| LDR | | 01794nmm uu200385 4500 |
| 001 | | 000000331333 |
| 005 | | 20240805164157 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438136175 |
| 035 | |
▼a (MiAaPQ)AAI10903792 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 020 |
| 100 | 1 |
▼a Ye, Jianbo. |
| 245 | 10 |
▼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. |
| 502 | 1 |
▼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 |
| 710 | 20 |
▼a The Pennsylvania State University.
▼b Information Sciences and Technology. |
| 773 | 0 |
▼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 |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000749
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |