자료유형 | 학위논문 |
---|---|
서명/저자사항 | High-Dimensional Structured Regression Using Convex Optimization. |
개인저자 | Yu, Guo. |
단체저자명 | Cornell University. Statistics. |
발행사항 | [S.l.]: Cornell University., 2018. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
형태사항 | 193 p. |
기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
ISBN | 9780438344778 |
학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Jacob Bien. |
요약 | While the term "Big Data" can have multiple meanings, we consider the type of data in which the number of features can be much greater than the number of observations (also known as high-dimensional data). High-dimensional data is abundant in co |
일반주제명 | Statistics. |
언어 | 영어 |
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: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |