Selective Combination and Management of Distributed Machine Learning Models

Takeshi Tsuchiya*, Ryuichi Mochizuki, Hiroo Hirose, Tetsuyasu Yamada, Keiichi Koyanagi, Quang Tran Minh

*この研究の対応する著者

研究成果: Conference contribution

抄録

This study presents a method for selecting and combining feature models constructed by the machine learning on the processing task capability. The evaluation of combining the feature models shows that the processing task capability can be improved by selecting and reaching feature models based on their similarity to the vector of queries without combining all feature models. Then, we discuss a method for constructing logical the R-Tree algorithm on the distributed fog nodes. For future work, we will implement the proposed method on various types of data.

本文言語English
ホスト出版物のタイトルFuture Data and Security Engineering - 8th International Conference, FDSE 2021, Proceedings
編集者Tran Khanh Dang, Josef Küng, Tai M. Chung, Makoto Takizawa
出版社Springer Science and Business Media Deutschland GmbH
ページ113-124
ページ数12
ISBN(印刷版)9783030913861
DOI
出版ステータスPublished - 2021
イベント8th International Conference on Future Data and Security Engineering , FDSE 2021 - Virtual, Online
継続期間: 2021 11月 242021 11月 26

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13076 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference8th International Conference on Future Data and Security Engineering , FDSE 2021
CityVirtual, Online
Period21/11/2421/11/26

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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