| LDR | | 01731nam u200517 4500 |
| 001 | | 000000467452 |
| 005 | | 20220223112655 |
| 008 | | 220131s2020 us ||||||||||||||c||eng d |
| 020 | |
▼a 9798534655759 |
| 035 | |
▼a (MiAaPQ)AAI28256587 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Ji, Disi. |
| 245 | 10 |
▼a Label-Efficient Bayesian Assessment of Black-Box Classifiers. |
| 260 | |
▼a [S.l.]:
▼b University of California, Irvine.,
▼c 2020. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2020. |
| 300 | |
▼a 140 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 83-02, Section: B. |
| 500 | |
▼a Advisor: Smyth, Padhraic. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Irvine, 2020. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 590 | |
▼a School code: 0030. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Probability. |
| 650 | 4 |
▼a Accuracy. |
| 650 | 4 |
▼a Active learning. |
| 650 | 4 |
▼a Datasets. |
| 650 | 4 |
▼a Algorithms. |
| 650 | 4 |
▼a Sensitivity analysis. |
| 650 | 4 |
▼a Advisors. |
| 650 | 4 |
▼a Estimates. |
| 650 | 4 |
▼a Experiments. |
| 650 | 4 |
▼a Calibration. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of California, Irvine.
▼b Computer Science - Ph.D.. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 83-02B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0030 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2020 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T16051121
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202202
▼f 2022 |
| 990 | |
▼a ***1012033 |
| 991 | |
▼a E-BOOK |