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Computational Techniques to Identify Rare Events in Spatio-Temporal Data.- [electronic resource] : Mithal, Varun.
Computational Techniques to Identify Rare Events in Spatio-Temporal Data.- [electronic resource] : Mithal, Varun.
상세정보
- 자료유형
- 학위논문(국외)
- 청구기호
- 저자명
- 서명/저자
- Computational Techniques to Identify Rare Events in Spatio-Temporal Data. - [electronic resource] : Mithal, Varun.
- 발행사항
- 발행사항
- 형태사항
- 1 online resource(109 p.)
- 주기사항
- Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
- 주기사항
- Adviser: Vipin Kumar.
- 학위논문주기
- Thesis (Ph.D.)--University of Minnesota, 2018.
- 초록/해제
- 요약Recent attention on the potential impacts of land cover changes to the environment as well as long-term climate change has increased the focus on automated tools for global-scale land surface monitoring. Advancements in remote sensing and data c
- 초록/해제
- 요약We study the problem of identifying land cover changes such as forest fires as a supervised binary classification task with the following characteristics: (i) instead of true labels only imperfect labels are available for training samples. These
- 초록/해제
- 요약We explore approaches to reduce errors in remote sensing based classification products, which are common due to poor data quality (eg., instrument failure, atmospheric interference) as well as limitations of the classification models. We present
- 일반주제명
- 기타저자
- 기본자료저록
- Dissertation Abstracts International. 79-12B(E).
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 원문정보보기
- 소장사항
-
201812 2019
MARC
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■00520190102172025
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■035 ▼a(MiAaPQ)AAI10823049
■035 ▼a(MiAaPQ)umn:19188
■040 ▼aMiAaPQ▼cMiAaPQ
■08204▼a004
■090 ▼a전자도서(박사논문)
■1001 ▼aMithal, Varun.
■24510▼aComputational Techniques to Identify Rare Events in Spatio-Temporal Data.▼h[electronic resource]▼cMithal, Varun.
■260 ▼a[S.l.]▼bUniversity of Minnesota. ▼c2018
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2018
■300 ▼a1 online resource(109 p.)
■500 ▼aSource: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
■500 ▼aAdviser: Vipin Kumar.
■5021 ▼aThesis (Ph.D.)--University of Minnesota, 2018.
■520 ▼aRecent attention on the potential impacts of land cover changes to the environment as well as long-term climate change has increased the focus on automated tools for global-scale land surface monitoring. Advancements in remote sensing and data c
■520 ▼aWe study the problem of identifying land cover changes such as forest fires as a supervised binary classification task with the following characteristics: (i) instead of true labels only imperfect labels are available for training samples. These
■520 ▼aWe explore approaches to reduce errors in remote sensing based classification products, which are common due to poor data quality (eg., instrument failure, atmospheric interference) as well as limitations of the classification models. We present
■590 ▼aSchool code: 0130.
■650 4▼aComputer science.
■690 ▼a0984
■71020▼aUniversity of Minnesota▼bComputer Science.
■7730 ▼tDissertation Abstracts International▼g79-12B(E).
■773 ▼tDissertation Abstract International
■790 ▼a0130
■791 ▼aPh.D.
■792 ▼a2018
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T14998538▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a201812▼f2019
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