자료유형 | 학위논문 |
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서명/저자사항 | Unsupervised Learning: Evaluation, Distributed Setting, and Privacy. |
개인저자 | Tsikhanovich, Maksim. |
단체저자명 | Rensselaer Polytechnic Institute. Computer Science. |
발행사항 | [S.l.]: Rensselaer Polytechnic Institute., 2018. |
발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
형태사항 | 134 p. |
기본자료 저록 | Dissertation Abstracts International 79-12B(E). Dissertation Abstract International |
ISBN | 9780438206403 |
학위논문주기 | Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018. |
일반주기 |
Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Malik Magdon-Ismail. |
요약 | Chapter 1 is an overview of topic modeling as a set of unsupervised learning tasks. We present the Latent Dirichlet Allocation (LDA) model, and show how k-means as well as non-negative matrix factorization (NMF) can also be interpreted as topic |
요약 | In Chapter 2 we present two algorithms for the data-distributed non-negative matrix factorization (NMF) task, and one for the singular value decomposition (SVD). In the offline setting, M parties have already computed NMF models of their local d |
요약 | In Chapter 3 we study empirical measures of Distributional Differential Privacy. We want to measure to what extent one participant in a distributed computation can correctly identify the presence of a single document in another participant's dat |
일반주제명 | Computer science. |
언어 | 영어 |
바로가기 |
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |