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Transparent Machine Learning: Theory and Computation- [electronic resource]
![Transparent Machine Learning: Theory and Computation - [electronic resource]](/Users/Baul/Images/book.png)
Transparent Machine Learning: Theory and Computation- [electronic resource]
상세정보
- 자료유형
- 학위논문파일 국외
- 최종처리일시
- 20240214101231
- ISBN
- 9798379909802
- DDC
- 004
- 저자명
- Covert, Ian C.
- 서명/저자
- Transparent Machine Learning: Theory and Computation - [electronic resource]
- 발행사항
- [S.l.]: : University of Washington., 2023
- 발행사항
- Ann Arbor : : ProQuest Dissertations & Theses,, 2023
- 형태사항
- 1 online resource(522 p.)
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
- 주기사항
- Advisor: Lee, Su-In.
- 학위논문주기
- Thesis (Ph.D.)--University of Washington, 2023.
- 사용제한주기
- This item must not be sold to any third party vendors.
- 초록/해제
- 요약Modern machine learning is driven primarily by black-box models, which provide superior performance but offer limited transparency into how predictions are made. For applications where it is important to understand how models make decisions, and to assist in model debugging and data-driven knowledge discovery, we require tools that can answer questions about what influences a model's behavior. This is the goal of explainable machine learning (XML), a subfield that develops tools to understand complex models from various perspectives, including feature importance, concept attribution and data valuation. This dissertation presents several contributions to the field of XML, with the main ideas organized into three parts: (i) a framework that enables a unified analysis of many current methods, including their links with information theory and model robustness; (ii) a suite of techniques to accelerate the computation of Shapley values, which are the basis of several popular algorithms; and (iii) a range of methods for performing feature selection with deep learning models, e.g., in unsupervised and adaptive settings. Many of these ideas are motivated by applications in computational biology and medicine, but they also represent fundamental tools and perspectives that are useful across a variety of domains.
- 일반주제명
- Computer science.
- 일반주제명
- Statistics.
- 키워드
- Explainability
- 키워드
- Interpretability
- 키워드
- Machine learning
- 기타저자
- University of Washington Computer Science and Engineering
- 기본자료저록
- Dissertations Abstracts International. 85-01B.
- 기본자료저록
- Dissertation Abstract International
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■006m o d
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■020 ▼a9798379909802
■035 ▼a(MiAaPQ)AAI30527525
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aCovert, Ian C.
■24510▼aTransparent Machine Learning: Theory and Computation▼h[electronic resource]
■260 ▼a[S.l.]:▼bUniversity of Washington. ▼c2023
■260 1▼aAnn Arbor :▼bProQuest Dissertations & Theses, ▼c2023
■300 ▼a1 online resource(522 p.)
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-01, Section: B.
■500 ▼aAdvisor: Lee, Su-In.
■5021 ▼aThesis (Ph.D.)--University of Washington, 2023.
■506 ▼aThis item must not be sold to any third party vendors.
■520 ▼aModern machine learning is driven primarily by black-box models, which provide superior performance but offer limited transparency into how predictions are made. For applications where it is important to understand how models make decisions, and to assist in model debugging and data-driven knowledge discovery, we require tools that can answer questions about what influences a model's behavior. This is the goal of explainable machine learning (XML), a subfield that develops tools to understand complex models from various perspectives, including feature importance, concept attribution and data valuation. This dissertation presents several contributions to the field of XML, with the main ideas organized into three parts: (i) a framework that enables a unified analysis of many current methods, including their links with information theory and model robustness; (ii) a suite of techniques to accelerate the computation of Shapley values, which are the basis of several popular algorithms; and (iii) a range of methods for performing feature selection with deep learning models, e.g., in unsupervised and adaptive settings. Many of these ideas are motivated by applications in computational biology and medicine, but they also represent fundamental tools and perspectives that are useful across a variety of domains.
■590 ▼aSchool code: 0250.
■650 4▼aComputer science.
■650 4▼aStatistics.
■653 ▼aExplainability
■653 ▼aFeature selection
■653 ▼aInformation theory
■653 ▼aInterpretability
■653 ▼aMachine learning
■690 ▼a0800
■690 ▼a0984
■690 ▼a0463
■71020▼aUniversity of Washington▼bComputer Science and Engineering.
■7730 ▼tDissertations Abstracts International▼g85-01B.
■773 ▼tDissertation Abstract International
■790 ▼a0250
■791 ▼aPh.D.
■792 ▼a2023
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16933318▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
■980 ▼a202402▼f2024
