| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000434045 |
| 005 | | 20200226135733 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781392898437 |
| 035 | |
▼a (MiAaPQ)AAI22619396 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 001 |
| 100 | 1 |
▼a Milutinovic, Mitar. |
| 245 | 10 |
▼a Towards Automatic Machine Learning Pipeline Design. |
| 260 | |
▼a [S.l.]:
▼b University of California, Berkeley.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 104 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-06, Section: B. |
| 500 | |
▼a Advisor: Song, Dawn. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a 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 |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-06B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15493621
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1008102 |
| 991 | |
▼a E-BOOK |