LDR | | 02018nmm uu200397 4500 |
001 | | 000000334259 |
005 | | 20240805175941 |
008 | | 181129s2018 |||||||||||||||||c||eng d |
020 | |
▼a 9780438169203 |
035 | |
▼a (MiAaPQ)AAI10825537 |
035 | |
▼a (MiAaPQ)ucsd:17534 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
082 | 0 |
▼a 004 |
100 | 1 |
▼a Tosh, Christopher. |
245 | 10 |
▼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. |
502 | 1 |
▼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 |
710 | 20 |
▼a University of California, San Diego.
▼b Computer Science. |
773 | 0 |
▼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 |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998774
▼n KERIS |
980 | |
▼a 201812
▼f 2019 |
990 | |
▼a 관리자 |