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020 ▼a 9780438417694
035 ▼a (MiAaPQ)AAI10837965
035 ▼a (MiAaPQ)iastate:17458
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 248032
0491 ▼f DP
0820 ▼a 658
1001 ▼a Pham, Hieu Trung.
24510 ▼a Generalized Weighting for Bagged Ensemblesbles.
260 ▼a [S.l.] : ▼b Iowa State University., ▼c 2018
260 1 ▼a Ann Arbor : ▼b ProQuest Dissertations & Theses, ▼c 2018
300 ▼a 87 p.
500 ▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B.
500 ▼a Adviser: Sigurdur Olafsson.
5021 ▼a Thesis (Ph.D.)--Iowa State University, 2018.
520 ▼a 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
520 ▼a In this dissertation, we focus our attention to bagged ensembles
520 ▼a 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
520 ▼a 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
590 ▼a School code: 0097.
650 4 ▼a Industrial engineering.
690 ▼a 0546
71020 ▼a Iowa State University. ▼b Industrial and Manufacturing Systems Engineering.
7730 ▼t Dissertation Abstracts International ▼g 80-02B(E).
773 ▼t Dissertation Abstract International
790 ▼a 0097
791 ▼a Ph.D.
792 ▼a 2018
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999599 ▼n KERIS
980 ▼a 201812 ▼f 2019
990 ▼a 관리자 ▼b 관리자