| LDR | | 01811nam u200385 4500 |
| 001 | | 000000421545 |
| 005 | | 20190215165412 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438324701 |
| 035 | |
▼a (MiAaPQ)AAI10816269 |
| 035 | |
▼a (MiAaPQ)berkeley:17848 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Jiang, Biye. |
| 245 | 10 |
▼a Exploratory Model Analysis for Machine Learning. |
| 260 | |
▼a [S.l.]:
▼b University of California, Berkeley.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 98 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B. |
| 500 | |
▼a Adviser: John Canny. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 520 | |
▼a Machine learning is growing in importance in many different fields. However, it is still very hard for users to tune hyper-parameters when optimizing their models, or perform a comprehensive and interpretable diagnosis for complex models like de |
| 520 | |
▼a We demonstrate the usage of our system in several real-world applications. For problems like advertisement optimization or clustering where multiple optimization objectives exist, users can incorporate secondary criteria into the model-generatio |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Computer science. |
| 690 | |
▼a 0984 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-01B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998235
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |