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Unsupervised Learning Methods in Digital Phenotyping
Unsupervised Learning Methods in Digital Phenotyping
상세정보
- 자료유형
- 학위논문파일 국외
- ISBN
- 9798534671643
- DDC
- 574
- 저자명
- Liu, Gang.
- 서명/저자
- Unsupervised Learning Methods in Digital Phenotyping
- 발행사항
- [Sl] : Harvard University, 2021
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2021
- 형태사항
- 75 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
- 주기사항
- Advisor: Onnela, Jukka-Pekka.
- 학위논문주기
- Thesis (Ph.D.)--Harvard University, 2021.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 일반주제명
- Biostatistics
- 일반주제명
- Electrical engineering
- 일반주제명
- Information technology
- 일반주제명
- Sample size
- 일반주제명
- Quality of life
- 일반주제명
- Simulation
- 일반주제명
- Statistics
- 일반주제명
- Software
- 일반주제명
- Diabetes
- 일반주제명
- Random variables
- 일반주제명
- Sensors
- 일반주제명
- Wearable computers
- 일반주제명
- Methods
- 일반주제명
- Algorithms
- 일반주제명
- Mental health
- 일반주제명
- Time series
- 일반주제명
- COVID-19
- 키워드
- Accelerometer
- 키워드
- GPS
- 키워드
- Missing data
- 키워드
- Smartphone
- 기타저자
- Harvard University Biostatistics
- 기본자료저록
- Dissertations Abstracts International. 83-02B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008220131s2021 us c eng d■020 ▼a9798534671643
■035 ▼a(MiAaPQ)AAI28498350
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a574
■1001 ▼aLiu, Gang.▼0(orcid)0000-0003-3544-363X
■24510▼aUnsupervised Learning Methods in Digital Phenotyping
■260 ▼a[Sl]▼bHarvard University▼c2021
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2021
■300 ▼a75 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 83-02, Section: B.
■500 ▼aAdvisor: Onnela, Jukka-Pekka.
■5021 ▼aThesis (Ph.D.)--Harvard University, 2021.
■506 ▼aThis item must not be sold to any third party vendors.
■590 ▼aSchool code: 0084.
■650 4▼aBiostatistics
■650 4▼aElectrical engineering
■650 4▼aInformation technology
■650 4▼aSample size
■650 4▼aQuality of life
■650 4▼aSimulation
■650 4▼aStatistics
■650 4▼aSoftware
■650 4▼aDiabetes
■650 4▼aRandom variables
■650 4▼aSensors
■650 4▼aWearable computers
■650 4▼aMethods
■650 4▼aAlgorithms
■650 4▼aMental health
■650 4▼aTime series
■650 4▼aCOVID-19
■653 ▼aAccelerometer
■653 ▼aAnomaly detection
■653 ▼aDigital phenotyping
■653 ▼aGPS
■653 ▼aMissing data
■653 ▼aSmartphone
■690 ▼a0308
■690 ▼a0544
■690 ▼a0489
■690 ▼a0463
■690 ▼a0347
■71020▼aHarvard University▼bBiostatistics.
■7730 ▼tDissertations Abstracts International▼g83-02B.
■773 ▼tDissertation Abstract International
■790 ▼a0084
■791 ▼aPh.D.
■792 ▼a2021
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16052134▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202202▼f2022
