| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000433453 |
| 005 | | 20200225142218 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781088352236 |
| 035 | |
▼a (MiAaPQ)AAI22583135 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 574 |
| 100 | 1 |
▼a Wang, Xiaoqian. |
| 245 | 10 |
▼a New Nonlinear Machine Learning Algorithms with Applications to Biomedical Data Science. |
| 260 | |
▼a [S.l.]:
▼b University of Pittsburgh.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 136 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Huang, Heng. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of Pittsburgh, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 506 | |
▼a This item must not be added to any third party search indexes. |
| 520 | |
▼a 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. |
| 590 | |
▼a School code: 0178. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Bioinformatics. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0715 |
| 710 | 20 |
▼a University of Pittsburgh.
▼b Swanson School of Engineering. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0178 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15492763
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |