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Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Tao, Shaozhe. |
| 단체저자명 | University of Minnesota. Industrial and Systems Engineering. |
| 서명/저자사항 | Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications. |
| 발행사항 | [S.l.] : University of Minnesota., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 189 p. |
| 소장본 주기 | School code: 0130. |
| ISBN | 9780438168909 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Advisers: Shuzhong Zhang |
| 요약 | Many problems in machine learning can be formulated using optimization models with constraints that are well structured. Driven in part by such applications, the need to solve very large scale optimization models is pushing the performance limit |
| 요약 | First, we study popular scalable methods on sparse structured models, including alternating direction method of multipliers, coordinate descent method, proximal gradient method and accelerated proximal gradient method. In contrast to many global |
| 요약 | Next we move on to group sparse structured model. We develop an inverse covariance estimator that can regularize for overlapping group sparsity, and provide better estimates, especially when the dimension size is much larger than the number of s |
| 요약 | Finally, we explore a certain low-rank structure in tensor. We construct the connection between the low-rank property in tensor and the group sparsity in its factor matrices. This provides a way to find a low-rank tensor decomposition via a regu |
| 일반주제명 | Industrial engineering. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14998648 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00028168 | 658 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
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