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020 ▼a 9780438169203
035 ▼a (MiAaPQ)AAI10825537
035 ▼a (MiAaPQ)ucsd:17534
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0820 ▼a 004
1001 ▼a Tosh, Christopher.
24510 ▼a Algorithms for Statistical and Interactive Learning Tasks.
260 ▼a [S.l.] : ▼b University of California, San Diego., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 252 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
500 ▼a Adviser: Sanjoy Dasgupta.
5021 ▼a Thesis (Ph.D.)--University of California, San Diego, 2018.
520 ▼a In the first part of this thesis, we examine the computational complexity of three fundamental statistical tasks: maximum likelihood estimation, maximum a posteriori estimation, and approximate posterior sampling. We show that maximum likelihood
520 ▼a In the second part of this thesis, we explore the behavior of a common sampling algorithm known as the Gibbs sampler. We show that in the context of Bayesian Gaussian mixture models, this algorithm can take a very long time to converge, even whe
520 ▼a In the third part of this thesis, we consider learning problems in which the learner is allowed to solicit interaction from a user. In the context of classification, we present an efficient active learning algorithm whose performance is guarante
590 ▼a School code: 0033.
650 4 ▼a Computer science.
690 ▼a 0984
71020 ▼a University of California, San Diego. ▼b Computer Science.
7730 ▼t Dissertation Abstracts International ▼g 79-12B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0033
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998774 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자