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Structured Learning with Parsimony in Measurements and Computations: Theory, Algorithms, and Applications
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Li, Xingguo. |
| 단체저자명 | University of Minnesota. Electrical Engineering. |
| 서명/저자사항 | Structured Learning with Parsimony in Measurements and Computations: Theory, Algorithms, and Applications. |
| 발행사항 | [S.l.] : University of Minnesota., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 309 p. |
| 소장본 주기 | School code: 0130. |
| ISBN | 9780438353886 |
| 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 |
| 기본자료 저록 | Dissertation Abstracts International80-01B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14999952 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00024687 | DP 621.3 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출불가(별치) |
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