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Exploratory Model Analysis for Machine Learning
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Exploratory Model Analysis for Machine Learning. |
| 개인저자 | Jiang, Biye. |
| 단체저자명 | University of California, Berkeley. Computer Science. |
| 발행사항 | [S.l.]: University of California, Berkeley., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 98 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-01B(E). Dissertation Abstract International |
| ISBN | 9780438324701 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Berkeley, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-01(E), Section: B.
Adviser: John Canny. |
| 요약 | 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 |
| 요약 | 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 |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
