상세정보
Export to Refworks부가기능
Towards Automatic Machine Learning Pipeline Design
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Towards Automatic Machine Learning Pipeline Design. |
| 개인저자 | Milutinovic, Mitar. |
| 단체저자명 | University of California, Berkeley. Computer Science. |
| 발행사항 | [S.l.]: University of California, Berkeley., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 104 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-06B. Dissertation Abstract International |
| ISBN | 9781392898437 |
| 학위논문주기 | Thesis (Ph.D.)--University of California, Berkeley, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-06, Section: B.
Advisor: Song, Dawn. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | The rapid increase in the amount of data collected is quickly shifting the bottleneck of making informed decisions from a lack of data to a lack of data scientists to help analyze the collected data. Moreover, the publishing rate of new potential solutions and approaches for data analysis has surpassed what a human data scientist can follow. At the same time, we observe that many tasks a data scientist performs during analysis could be automated. Automatic machine learning (AutoML) research and solutions attempt to automate portions or even the entire data analysis process.We address two challenges in AutoML research: first, how to represent ML programs suitably for metalearning |
| 일반주제명 | Computer science. Artificial intelligence. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
