| LDR | | 03020nam u200481 4500 |
| 001 | | 000000421000 |
| 005 | | 20190215164947 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438423541 |
| 035 | |
▼a (MiAaPQ)AAI10810476 |
| 035 | |
▼a (MiAaPQ)upenngdas:13267 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 621.3 |
| 100 | 1 |
▼a Varol, Erdem. |
| 245 | 10 |
▼a Advancing Statistical Inference for Population Studies in Neuroimaging Using Machine Learning. |
| 260 | |
▼a [S.l.]:
▼b University of Pennsylvania.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 209 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: Christos Davatzikos. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Pennsylvania, 2018. |
| 520 | |
▼a Modern neuroimaging techniques allow us to investigate the brain in vivo and in high resolution, providing us with high dimensional information regarding the structure and the function of the brain in health and disease. Statistical analysis tec |
| 520 | |
▼a A prevalent area of research in neuroimaging is group comparison, i.e., the comparison of the imaging data of two groups (e.g. patients vs. healthy controls or people who respond to treatment vs. people who don't) to identify discriminative imag |
| 520 | |
▼a However, existing statistical methods are limited by their reliance on ad-hoc assumptions regarding the homogeneity of disease effect, spatial properties of the underlying signal and the covariate structure of data, which imposes certain constra |
| 520 | |
▼a The goal of this thesis is to address each of the aforementioned assumptions and limitations by introducing robust mathematical formulations, which are founded on multivariate machine learning techniques that integrate discriminative and generat |
| 520 | |
▼a Specifically, 1. First, we introduce an algorithm termed HYDRA which stands for heterogeneity through discriminative analysis . This method parses the heterogeneity in neuroimaging studies by simultaneously performing clustering and classificat |
| 520 | |
▼a We extensively validated the performance of the developed frameworks in the presence of diverse types of simulated scenarios. Furthermore, we applied our methods on a large number of clinical datasets that included structural and functional neur |
| 590 | |
▼a School code: 0175. |
| 650 | 4 |
▼a Electrical engineering. |
| 650 | 4 |
▼a Neurosciences. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0544 |
| 690 | |
▼a 0317 |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a University of Pennsylvania.
▼b Electrical and Systems Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0175 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997944
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |