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Algorithmic Advances in Learning from Large Dimensional Matrices and Scientific Data
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Ubaru, Shashanka. |
| 단체저자명 | University of Minnesota. Computer Science. |
| 서명/저자사항 | Algorithmic Advances in Learning from Large Dimensional Matrices and Scientific Data. |
| 발행사항 | [S.l.] : University of Minnesota., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 211 p. |
| 소장본 주기 | School code: 0130. |
| ISBN | 9780438168695 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Yousef Saad. |
| 요약 | This thesis is devoted to answering a range of questions in machine learning and data analysis related to large dimensional matrices and scientific data. Two key research objectives connect the different parts of the thesis: (a) development of f |
| 요약 | The first of the three parts of this thesis explores numerical linear algebra tools to develop efficient algorithms for machine learning with reduced computation cost and improved scalability. Here, we first develop inexpensive algorithms combin |
| 요약 | The second part of this thesis focuses on exploring novel non-traditional applications of information theory and codes, particularly in solving problems related to machine learning and high dimensional data analysis. Here, we first propose new m |
| 요약 | The third part of the thesis focuses on devising robust and stable learning algorithms, which yield results that are interpretable from specific scientific application viewpoint. We present Union of Intersections (UoI), a flexible, modular, and |
| 일반주제명 | Computer science. Mathematics. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14998557 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00024131 | DP 004 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출불가(별치) |
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