| LDR | | 02654nam u200469 4500 |
| 001 | | 000000419736 |
| 005 | | 20190215163920 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438345256 |
| 035 | |
▼a (MiAaPQ)AAI10929284 |
| 035 | |
▼a (MiAaPQ)cornellgrad:11115 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Jin, Ze.
▼0 (orcid)0000-0002-5882-6580. |
| 245 | 10 |
▼a Measuring Statistical Dependence and its Applications in Machine Learning. |
| 260 | |
▼a [S.l.]:
▼b Cornell University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 94 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: David S. Matteson. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Cornell University, 2018. |
| 520 | |
▼a 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: |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0058. |
| 650 | 4 |
▼a Statistics. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Mathematics. |
| 690 | |
▼a 0463 |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0405 |
| 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=T15000936
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |