| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000330360 |
| 005 | | 20241029143712 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438353886 |
| 035 | |
▼a (MiAaPQ)AAI10843849 |
| 035 | |
▼a (MiAaPQ)umn:19501 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Li, Xingguo. |
| 245 | 10 |
▼a Structured Learning with Parsimony in Measurements and Computations: Theory, Algorithms, and Applications. |
| 260 | |
▼a [S.l.] :
▼b University of Minnesota.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 309 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Jarvis D. Haupt. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Minnesota, 2018. |
| 520 | |
▼a In modern "Big Data" applications, structured learning is the most widely employed methodology. Within this paradigm, the fundamental challenge lies in developing practical, effective algorithmic inference methods. Often (e.g., deep learning) su |
| 520 | |
▼a Toward this end, we make efforts to investigate the theoretical properties of models and algorithms that present significant improvement in measurement and computation requirement. In particular, we first develop randomized approaches for dimens |
| 590 | |
▼a School code: 0130. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Computer engineering. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0464 |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of Minnesota.
▼b Electrical Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0130 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999952
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |