LDR | | 01768nam u200481 4500 |
001 | | 000000469811 |
005 | | 20220223114805 |
008 | | 220131s2021 us ||||||||||||||c||eng d |
020 | |
▼a 9798492753658 |
035 | |
▼a (MiAaPQ)AAI28718099 |
040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
082 | 0 |
▼a 621.3 |
100 | 1 |
▼a Hu, Hanbin. |
245 | 10 |
▼a Machine Learning Techniques for Rare Failure Detection in Analog and Mixed-Signal Verification and Test. |
260 | |
▼a [S.l.]:
▼b University of California, Santa Barbara.,
▼c 2021. |
260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2021. |
300 | |
▼a 192 p. |
500 | |
▼a Source: Dissertations Abstracts International, Volume: 83-05, Section: B. |
500 | |
▼a Advisor: Li, Peng. |
502 | 1 |
▼a Thesis (Ph.D.)--University of California, Santa Barbara, 2021. |
506 | |
▼a This item must not be sold to any third party vendors. |
590 | |
▼a School code: 0035. |
650 | 4 |
▼a Computer engineering. |
650 | 4 |
▼a Artificial intelligence. |
650 | 4 |
▼a Computer science. |
690 | |
▼a 0464 |
690 | |
▼a 0800 |
690 | |
▼a 0984 |
710 | 20 |
▼a University of California, Santa Barbara.
▼b Electrical & Computer Engineering. |
773 | 0 |
▼t Dissertations Abstracts International
▼g 83-05B. |
773 | |
▼t Dissertation Abstract International |
790 | |
▼a 0035 |
791 | |
▼a Ph.D. |
792 | |
▼a 2021 |
793 | |
▼a English |
856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T16054363
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
980 | |
▼a 202202
▼f 2022 |
990 | |
▼a ***1012033 |
991 | |
▼a E-BOOK |