| LDR | | 01997nam u200397 4500 |
| 001 | | 000000419719 |
| 005 | | 20190215163911 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438344921 |
| 035 | |
▼a (MiAaPQ)AAI10928741 |
| 035 | |
▼a (MiAaPQ)cornellgrad:11086 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Yan, Xiaohan. |
| 245 | 10 |
▼a Statistical Learning for Structural Patterns with Trees. |
| 260 | |
▼a [S.l.]:
▼b Cornell University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 169 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Jacob Bien. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Cornell University, 2018. |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0058. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a Cornell University.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0058 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15000916
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |