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Topological Based Machine Learning Methods
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Topological Based Machine Learning Methods. |
| 개인저자 | Georges, Alex. |
| 단체저자명 | University of California, San Diego. Physics. |
| 발행사항 | [S.l.]: University of California, San Diego., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 186 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781085597517 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, San Diego, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Meyer, David |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | This dissertation presents novel approaches and applications of machine learning architectures. In particular, these approaches are based on tools from topological data analysis and are used in conjunction with conventional machine learning methods. Topological data analysis, which is based on algebraic topology, can identify significant global mathematical structures which are out of reach of many other approaches. When we use topology we benefit from generality, and when we use conventional methods we benefit from specificity.This dissertation contains a broad overview of data science and topological data analysis, then transitions to three distinct machine learning applications of these methods. The first application uses linear methods to discover the inherent dimensionality of the manifold given by congressional roll call votes. The second uses persistent homology to identify extremely noisy images in both supervised and unsupervised tasks. The last application uses mapper objects to produce robust classification algorithms. Two additional projects are presented later in the appendix, and are related to the three main applications. The first of these constructs a method to choose optimal optimizers, and the second places mathematical constraints on the structure of renormalization group flows. |
| 일반주제명 | Applied mathematics. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
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