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Sets as Measures: Optimization and Machine Learning
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Sets as Measures: Optimization and Machine Learning. |
| 개인저자 | Boyd, Nicholas. |
| 단체저자명 | University of California, Berkeley. Statistics. |
| 발행사항 | [S.l.]: University of California, Berkeley., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 98 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438324633 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 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 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
