| LDR | | 01867nmm uu200445 4500 |
| 001 | | 000000333307 |
| 005 | | 20240805172802 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438324633 |
| 035 | |
▼a (MiAaPQ)AAI10816068 |
| 035 | |
▼a (MiAaPQ)berkeley:17836 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 082 | 0 |
▼a 001.5 |
| 100 | 1 |
▼a Boyd, Nicholas. |
| 245 | 10 |
▼a Sets as Measures: Optimization and Machine Learning. |
| 260 | |
▼a [S.l.] :
▼b University of California, Berkeley.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 98 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Advisers: Michael Jordan |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 520 | |
▼a The purpose of this thesis is to address the following simple question: |
| 520 | |
▼a 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?. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Artificial intelligence. |
| 650 | 4 |
▼a Statistics. |
| 650 | 4 |
▼a Applied mathematics. |
| 690 | |
▼a 0800 |
| 690 | |
▼a 0463 |
| 690 | |
▼a 0364 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998218
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자 |