| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000334634 |
| 005 | | 20250123112433 |
| 008 | | 181129s2017 ||| | | | eng d |
| 020 | |
▼a 9780438373914 |
| 035 | |
▼a (MiAaPQ)AAI10608668 |
| 035 | |
▼a (MiAaPQ)wisc:14701 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Zhang, Luwan. |
| 245 | 10 |
▼a Topics on Euclidean Distance Matrix and Unsupervised Ensemble Learning. |
| 260 | |
▼a [S.l.] :
▼b The University of Wisconsin - Madison.,
▼c 2017 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2017 |
| 300 | |
▼a 103 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Ming Yuan. |
| 502 | 1 |
▼a Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017. |
| 520 | |
▼a This thesis is devoted to the study of Euclidean distance matrix and unsupervised ensemble learning under the high-dimensional setting. It consists of three pieces of work, focusing on proposing a shrinkage estimator of Euclidean distance matrix |
| 520 | |
▼a In the first part of thesis, we discuss the problem of recovering an Euclidean distance matrix from noisy or imperfect observations of pairwise dissimilarity scores between a set of objects. This problem naturally arises in many different contex |
| 520 | |
▼a As a sequel of Chapter 1, the second part pays attention to conducting statistical analyses after mapping a set of objects from an arbitrary domain to the Euclidean space. In this chapter, we specifically consider the generalization of ANOVA mod |
| 520 | |
▼a The third part mainly concerns developing a new ensemble method for classification problems when the true class labels are not available (a.k.a unsupervised setting). The motivation arises from an intrinsic drawback of crowdsourcing, in which an |
| 520 | |
▼a Finally, we conclude the thesis in Chapter 4. |
| 590 | |
▼a School code: 0262. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a The University of Wisconsin - Madison.
▼b Statistics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0262 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2017 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14996627
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |