LDR | | 02025nmm uu200397 4500 |
001 | | 000000332285 |
005 | | 20240805170524 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
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▼a 9780438206403 |
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▼a (MiAaPQ)AAI10751762 |
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▼a (MiAaPQ)rpi:11252 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 004 |
100 | 1 |
▼a Tsikhanovich, Maksim. |
245 | 10 |
▼a Unsupervised Learning: Evaluation, Distributed Setting, and Privacy. |
260 | |
▼a [S.l.] :
▼b Rensselaer Polytechnic Institute.,
▼c 2018 |
260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
300 | |
▼a 134 p. |
500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
500 | |
▼a Adviser: Malik Magdon-Ismail. |
502 | 1 |
▼a Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2018. |
520 | |
▼a 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 |
520 | |
▼a 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 |
520 | |
▼a 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 |
590 | |
▼a School code: 0185. |
650 | 4 |
▼a Computer science. |
690 | |
▼a 0984 |
710 | 20 |
▼a Rensselaer Polytechnic Institute.
▼b Computer Science. |
773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0185 |
791 | |
▼a Ph.D. |
792 | |
▼a 2018 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997192
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |