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Unsupervised Learning: Evaluation, Distributed Setting, and Privacy
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| 자료유형 | E-Book |
|---|---|
| 개인저자 | Tsikhanovich, Maksim. |
| 단체저자명 | Rensselaer Polytechnic Institute. Computer Science. |
| 서명/저자사항 | Unsupervised Learning: Evaluation, Distributed Setting, and Privacy. |
| 발행사항 | [S.l.] : Rensselaer Polytechnic Institute., 2018 |
| 발행사항 | Ann Arbor : ProQuest Dissertations & Theses, 2018 |
| 형태사항 | 134 p. |
| 소장본 주기 | School code: 0185. |
| ISBN | 9780438206403 |
| 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 |
| 기본자료 저록 | Dissertation Abstracts International79-12B(E). Dissertation Abstract International |
| 대출바로가기 | http://www.riss.kr/pdu/ddodLink.do?id=T14997192 |
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| No. | 등록번호 | 청구기호 | 소장처 | 도서상태 | 반납예정일 | 예약 | 서비스 | 매체정보 |
|---|---|---|---|---|---|---|---|---|
| 1 | WE00026612 | 004 | 가야대학교// | 대출가능 |
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