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Sets as Measures: Optimization and Machine Learning
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Boyd, Nicholas. |
| 단체저자명 | University of California, Berkeley. Statistics. |
| 서명/저자사항 | Sets as Measures: Optimization and Machine Learning. |
| 발행사항 | [S.l.] : University of California, Berkeley., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 98 p. |
| 소장본 주기 | School code: 0028. |
| ISBN | 9780438324633 |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Advisers: Michael Jordan |
| 요약 | The purpose of this thesis is to address the following simple question: |
| 요약 | How do we design efficient algorithms to solve optimization or machine learning problems where the decision variable (or target label) is a set of unknown cardinality?. |
| 요약 | In this thesis we show that, in some cases, optimization and machine learning algorithms designed to work with single vectors can be directly applied to problems involving sets. We do this by invoking a classical trick: we lift sets to elements |
| 일반주제명 | Artificial intelligence. Statistics. Applied mathematics. |
| 언어 | 영어 |
| 기본자료 저록 | Dissertation Abstracts International80-01B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14998218 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00027634 | 001.5 | 가야대학교/전자책서버(컴퓨터서버)/ | 대출가능 |
|
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