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Structured Learning with Parsimony in Measurements and Computations: Theory, Algorithms, and Applications
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Structured Learning with Parsimony in Measurements and Computations: Theory, Algorithms, and Applications. |
| 개인저자 | Li, Xingguo. |
| 단체저자명 | University of Minnesota. Electrical Engineering. |
| 발행사항 | [S.l.]: University of Minnesota., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 309 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438353886 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Jarvis D. Haupt. |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Electrical engineering. Computer engineering. Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
