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Resource-Efficient Machine Learning via Count-Sketches and Locality-Sensitive Hashing (LSH)
Resource-Efficient Machine Learning via Count-Sketches and Locality-Sensitive Hashing (LSH)
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20220210094501
- ISBN
- 9798535523965
- DDC
- 004
- 서명/저자
- Resource-Efficient Machine Learning via Count-Sketches and Locality-Sensitive Hashing (LSH)
- 발행사항
- [Sl] : Rice University, 2020
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2020
- 형태사항
- 167 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 83-04, Section: B.
- 주기사항
- Advisor: Shrivastava, Anshumali.
- 학위논문주기
- Thesis (Ph.D.)--Rice University, 2020.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 일반주제명
- Computer science
- 일반주제명
- Sparsity
- 일반주제명
- Sample size
- 일반주제명
- Accuracy
- 일반주제명
- Datasets
- 일반주제명
- Experiments
- 일반주제명
- Classification
- 일반주제명
- Variables
- 일반주제명
- Approximation
- 일반주제명
- Feature selection
- 일반주제명
- Algorithms
- 일반주제명
- Artificial intelligence
- 일반주제명
- Electrical engineering
- 키워드
- Deep learning
- 키워드
- Machine learning
- 키워드
- Count-sketch
- 키워드
- Meta-genomics
- 기타저자
- Rice University Computer Science
- 기본자료저록
- Dissertations Abstracts International. 83-04B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
008220131s2020 us c eng d■001000016054810
■00520220210094501
■020 ▼a9798535523965
■035 ▼a(MiAaPQ)AAI28735955
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aSpring, Ryan Daniel.
■24510▼aResource-Efficient Machine Learning via Count-Sketches and Locality-Sensitive Hashing (LSH)
■260 ▼a[Sl]▼bRice University▼c2020
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2020
■300 ▼a167 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 83-04, Section: B.
■500 ▼aAdvisor: Shrivastava, Anshumali.
■5021 ▼aThesis (Ph.D.)--Rice University, 2020.
■506 ▼aThis item must not be sold to any third party vendors.
■590 ▼aSchool code: 0187.
■650 4▼aComputer science
■650 4▼aSparsity
■650 4▼aSample size
■650 4▼aAccuracy
■650 4▼aDatasets
■650 4▼aExperiments
■650 4▼aClassification
■650 4▼aVariables
■650 4▼aApproximation
■650 4▼aFeature selection
■650 4▼aAlgorithms
■650 4▼aArtificial intelligence
■650 4▼aElectrical engineering
■653 ▼aDeep learning
■653 ▼aMachine learning
■653 ▼aLocality-sensitive hashing
■653 ▼aCount-sketch
■653 ▼aStochastic optimization
■653 ▼aNatural language processing
■653 ▼aQuestion answering
■653 ▼aQuestion generation
■653 ▼aMeta-genomics
■653 ▼aFeature delection
■653 ▼aMutual information
■653 ▼aImportance sampling
■653 ▼aPartition function
■653 ▼aSoftmax classifier
■690 ▼a0984
■690 ▼a0544
■690 ▼a0800
■71020▼aRice University▼bComputer Science.
■7730 ▼tDissertations Abstracts International▼g83-04B.
■773 ▼tDissertation Abstract International
■790 ▼a0187
■791 ▼aPh.D.
■792 ▼a2020
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16054810▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202202▼f2022
