Presentation 2016-11-16
[Poster Presentation] Optimization Method of Deep Ensemble Learning using Hierarchical Clustering
Natsuki Koda, Sumio Watanabe,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) The method which is used for prediction by combining many different learning machines generated by using same training data is referred to as ensemble learning. In ensemble learning using different results derived from different initial values, it has been needed to combine many locally optimal parameters. In this paper, we propose a method which realizes accurate ensemble learning using few learning machines by applying hierarchical clustering to the set of locally optimal parameters and choosing many objective parameters, and show it's effectiveness through artificial data experiment.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Ensemble learning / Hierarchical clustering / Generalization error
Paper # IBISML2016-70
Date of Issue 2016-11-09 (IBISML)

Conference Information
Committee IBISML
Conference Date 2016/11/16(3days)
Place (in Japanese) (See Japanese page)
Place (in English) Kyoto Univ.
Topics (in Japanese) (See Japanese page)
Topics (in English) Information-Based Induction Science Workshop (IBIS2016)
Chair Kenji Fukumizu(ISM)
Vice Chair Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Kyoto Univ.)
Secretary Masashi Sugiyama(Univ. of Tokyo) / Hisashi Kashima(Nagoya Inst. of Tech.)
Assistant Toshihiro Kamishima(AIST) / Tomoharu Iwata(NTT)

Paper Information
Registration To Technical Committee on Infomation-Based Induction Sciences and Machine Learning
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) [Poster Presentation] Optimization Method of Deep Ensemble Learning using Hierarchical Clustering
Sub Title (in English)
Keyword(1) Ensemble learning
Keyword(2) Hierarchical clustering
Keyword(3) Generalization error
1st Author's Name Natsuki Koda
1st Author's Affiliation Tokyo Institute of Technology(Tokyo Tech)
2nd Author's Name Sumio Watanabe
2nd Author's Affiliation Tokyo Institute of Technology(Tokyo Tech)
Date 2016-11-16
Paper # IBISML2016-70
Volume (vol) vol.116
Number (no) IBISML-300
Page pp.pp.171-176(IBISML),
#Pages 6
Date of Issue 2016-11-09 (IBISML)