자료유형 | 학위논문 |
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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 |
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