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Algorithmic Advances in Learning from Large Dimensional Matrices and Scientific Data
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Algorithmic Advances in Learning from Large Dimensional Matrices and Scientific Data. |
| 개인저자 | Ubaru, Shashanka. |
| 단체저자명 | University of Minnesota. Computer Science. |
| 발행사항 | [S.l.]: University of Minnesota., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 211 p. |
| 기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
| ISBN | 9780438168695 |
| 학위논문주기 | Thesis (Ph.D.)--University of Minnesota, 2018. |
| 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 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
