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Statistical Learning for Structural Patterns with Trees
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Statistical Learning for Structural Patterns with Trees. |
| 개인저자 | Yan, Xiaohan. |
| 단체저자명 | Cornell University. Statistics. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 169 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438344921 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Jacob Bien. |
| 요약 | In achieving structural patterns in parameters, we focus on two challenging cases in which (1) hierarchical sparsity pattern is desired such that one group of parameters is set to zero whenever another is set to zero |
| 요약 | For achieving hierarchical sparsity patterns in parameters, we investigate the differences between group lasso (GL) and latent overlapping group lasso (LOG) in terms of their statistical properties and computational efficiency. We highlight a ph |
| 요약 | Another kind of sparsity we care about is sparsity in the data itself. It is prevalent to have many highly sparse features for counting frequency of rare events in diverse areas, ranging from natural language processing (e.g., rare words) to bio |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
