상세정보
Export to Refworks부가기능
Measuring Statistical Dependence and its Applications in Machine Learning
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Measuring Statistical Dependence and its Applications in Machine Learning. |
| 개인저자 | Jin, Ze. |
| 단체저자명 | Cornell University. Statistics. |
| 발행사항 | [S.l.]: Cornell University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 94 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438345256 |
| 학위논문주기 | Thesis (Ph.D.)--Cornell University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: David S. Matteson. |
| 요약 | My PhD research focuses on measuring and testing mutual dependence and conditional mean dependence, and applying it to Machine Learning problems, which is elaborated in the following four chapters: |
| 요약 | Chapter 1 -- We propose three new measures of mutual dependence between multiple random vectors. Each measure is zero if and only if the random vectors are mutually independent. The first generalizes distance covariance from pairwise dependence |
| 요약 | Chapter 2 -- We apply both distance-based and kernel-based mutual dependence measures to independent component analysis (ICA), and generalize dCovICA to MDMICA, minimizing empirical dependence measures as an objective function in both deflation |
| 요약 | Chapter 3 -- Independent component analysis (ICA) decomposes multivariate data into mutually independent components (ICs). The ICA model is subject to a constraint that at most one of these components is Gaussian, which is required for model ide |
| 요약 | Chapter 4 -- A crucial problem in statistics is to decide whether additional variables are needed in a regression model. We propose a new multivariate test to investigate the conditional mean independence of Y given X conditioning on some known |
| 일반주제명 | Statistics. Computer science. Mathematics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
