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Generalized Weighting for Bagged Ensemblesbles
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Generalized Weighting for Bagged Ensemblesbles. |
| 개인저자 | Pham, Hieu Trung. |
| 단체저자명 | Iowa State University. Industrial and Manufacturing Systems Engineering. |
| 발행사항 | [S.l.]: Iowa State University., 2018. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2018. |
| 형태사항 | 87 p. |
| 기본자료 저록 | Dissertation Abstracts International 80-02B(E). Dissertation Abstract International |
| ISBN | 9780438417694 |
| 학위논문주기 | Thesis (Ph.D.)--Iowa State University, 2018. |
| 일반주기 |
Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
Adviser: Sigurdur Olafsson. |
| 요약 | Ensemble learning is a popular classification method where many individual simple learners contribute to a final prediction. Constructing an ensemble of learners has been shown to consistently improve prediction accuracy over a single learner. T |
| 요약 | In this dissertation, we focus our attention to bagged ensembles |
| 요약 | Going a step further we generalize our weights such that we allow simultaneous control over bias and variance. In particular, we introduce a regularization term that controls the variance reduction for bagged ensembles. Therefore, a new tunable |
| 요약 | To aid in the applicability of this body of work, the author discusses an R package that allows users to implement our proposed weighting scheme to arbitrary bagged ensembles. The package provides tools for constructing tunable bagged ensembles |
| 일반주제명 | Industrial engineering. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
