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Making Bayesian Optimization Practical in the Context of High Dimensional, Highly Expensive, BlackBox Functions
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Making Bayesian Optimization Practical in the Context of High Dimensional, Highly Expensive, BlackBox Functions. |
| 개인저자 | Mathesen, Logan. |
| 단체저자명 | Arizona State University. Industrial Engineering. |
| 발행사항 | [S.l.]: Arizona State University., 2021. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2021. |
| 형태사항 | 149 p. |
| 기본자료 저록 | Dissertations Abstracts International 83-03B. Dissertation Abstract International |
| ISBN | 9798535542720 |
| 학위논문주기 | Thesis (Ph.D.)--Arizona State University, 2021. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Advisor: Pedrielli, Giulia. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 일반주제명 | Operations research. Statistics. Computer science. Mathematical programming. Simulation. Exploitation. Optimization techniques. Input output. Neural networks. Dissertations & theses. Explicit knowledge. Linear programming. Methods. Confidence intervals. Optimization algorithms. Information sources. Weightlifting. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
