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Detecting and Leveraging Changes in Temporal Data
Detecting and Leveraging Changes in Temporal Data
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105549
- ISBN
- 9798265473776
- DDC
- 004
- 저자명
- Ahad, Nauman.
- 서명/저자
- Detecting and Leveraging Changes in Temporal Data
- 발행사항
- [Sl] : Georgia Institute of Technology, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 168 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-06, Section: A.
- 주기사항
- Advisor: Davenport, Mark.
- 학위논문주기
- Thesis (Ph.D.)--Georgia Institute of Technology, 2024.
- 초록/해제
- 요약The ability to identify distributional shifts in temporal data is a common theme for both change-point detection and various machine learning tasks. This thesis explores the interplay between machine learning and change-point detection, and uses this interplay to devise new methods that utilize change-point detection for improving machine learning and in the reverse case, utilizes machine-learning to devise improved change point detection methods. For improving change-point detection, we explore how supervision can help learn distance metrics for designing robust change-point detection methods. For improving machine learning, we explore how change-points can provide weak supervision for supervised machine learning tasks. We also investigate how machine learning models can adapt to distributional changes by learning to selectively mask time series channels with significant shifts.
- 일반주제명
- Computer science
- 일반주제명
- Information science
- 키워드
- Temporal data
- 키워드
- Machine learning
- 기본자료저록
- Dissertations Abstracts International. 87-06A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798265473776
■035 ▼a(MiAaPQ)AAI32315657
■035 ▼a(MiAaPQ)GeorgiaTech75667
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aAhad, Nauman.
■24510▼aDetecting and Leveraging Changes in Temporal Data
■260 ▼a[Sl]▼bGeorgia Institute of Technology▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a168 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-06, Section: A.
■500 ▼aAdvisor: Davenport, Mark.
■5021 ▼aThesis (Ph.D.)--Georgia Institute of Technology, 2024.
■520 ▼aThe ability to identify distributional shifts in temporal data is a common theme for both change-point detection and various machine learning tasks. This thesis explores the interplay between machine learning and change-point detection, and uses this interplay to devise new methods that utilize change-point detection for improving machine learning and in the reverse case, utilizes machine-learning to devise improved change point detection methods. For improving change-point detection, we explore how supervision can help learn distance metrics for designing robust change-point detection methods. For improving machine learning, we explore how change-points can provide weak supervision for supervised machine learning tasks. We also investigate how machine learning models can adapt to distributional changes by learning to selectively mask time series channels with significant shifts.
■590 ▼aSchool code: 0078.
■650 4▼aComputer science
■650 4▼aInformation science
■653 ▼aTemporal data
■653 ▼aMachine learning
■690 ▼a0984
■690 ▼a0723
■71020▼aGeorgia Institute of Technology.
■7730 ▼tDissertations Abstracts International▼g87-06A.
■790 ▼a0078
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17360575▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
