2016年
Ensemble-Based Local Learning for High-Dimensional Data Regression
2016 23RD INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)
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- 開始ページ
- 2640
- 終了ページ
- 2645
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/ICPR.2016.7900033
- 出版者・発行元
- IEEE COMPUTER SOC
In this paper we propose a new local learning based regression method which utilizes ensemble-learning as a form of regularization to reduce the variance of local estimators. This makes it possible to use local learning methods even with very high-dimensional datasets. The efficacy of the proposed method is illustrated on two publicly available high-dimensional sets in comparison with several global learning methods, and it is shown that the proposed ensemble-based local learning method significantly outperforms the global ones.
- リンク情報
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- DOI
- https://doi.org/10.1109/ICPR.2016.7900033
- DBLP
- https://dblp.uni-trier.de/rec/conf/icpr/RaytchevKKTK16
- Web of Science
- https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000406771302103&DestApp=WOS_CPL
- URL
- http://dblp.uni-trier.de/db/conf/icpr/icpr2016.html#conf/icpr/RaytchevKKTK16
- ID情報
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- DOI : 10.1109/ICPR.2016.7900033
- ISSN : 1051-4651
- DBLP ID : conf/icpr/RaytchevKKTK16
- Web of Science ID : WOS:000406771302103