상세정보
Export to Refworks부가기능
Topics on Euclidean Distance Matrix and Unsupervised Ensemble Learning
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Topics on Euclidean Distance Matrix and Unsupervised Ensemble Learning. |
| 개인저자 | Zhang, Luwan. |
| 단체저자명 | The University of Wisconsin - Madison. Statistics. |
| 발행사항 | [S.l.]: The University of Wisconsin - Madison., 2017. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2017. |
| 형태사항 | 103 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438373914 |
| 학위논문주기 | Thesis (Ph.D.)--The University of Wisconsin - Madison, 2017. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: Ming Yuan. |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | 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 |
| 요약 | Finally, we conclude the thesis in Chapter 4. |
| 일반주제명 | Statistics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
