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New Nonlinear Machine Learning Algorithms with Applications to Biomedical Data Science
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | New Nonlinear Machine Learning Algorithms with Applications to Biomedical Data Science. |
| 개인저자 | Wang, Xiaoqian. |
| 단체저자명 | University of Pittsburgh. Swanson School of Engineering. |
| 발행사항 | [S.l.]: University of Pittsburgh., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 136 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04B. Dissertation Abstract International |
| ISBN | 9781088352236 |
| 학위논문주기 | Thesis (Ph.D.)--University of Pittsburgh, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: B.
Advisor: Huang, Heng. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | Recent advances in machine learning have spawned innovation and prosperity in various fields. In machine learning models, nonlinearity facilitates more flexibility and ability to better fit the data. However, the improved model flexibility is often accompanied by challenges such as overfitting, higher computational complexity, and less interpretability. Thus, it is an important problem of how to design new feasible nonlinear machine learning models to address the above different challenges posed by various data scales, and bringing new discoveries in both theory and applications. In this thesis, we propose several newly designed nonlinear machine learning algorithms, such as additive models and deep learning methods, to address these challenges and validate the new models via the emerging biomedical applications.First, we introduce new interpretable additive models for regression and classification and address the overfitting problem of nonlinear models in small and medium scale data. we derive the model convergence rate under mild conditions in the hypothesis space and uncover new potential biomarkers in Alzheimer's disease study. Second, we propose a deep generative adversarial network to analyze the temporal correlation structure in longitudinal data and achieve state-of-the-art performance in Alzheimer's early diagnosis. Meanwhile, we design a new interpretable neural network model to improve the interpretability of the results of deep learning methods. Further, to tackle the insufficient labeled data in large-scale data analysis, we design a novel semi-supervised deep learning model and validate the performance in the application of gene expression inference. |
| 일반주제명 | Computer science. Bioinformatics. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
