Clustering classifiers learnt from local datasets based on cosine similarity

Kaikai Zhao, Einoshin Suzuki

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

3 被引用数 (Scopus)

抄録

In this paper we present a new method to measure the degree of dissimilarity of a pair of linear classifiers. This method is based on the cosine similarity between the normal vectors of the hyperplanes of the linear classifiers. A significant advantage of this method is that it has a good interpretation and requires very little information to exchange among datasets. Evaluations on a synthetic dataset, a dataset from the UCI Machine Learning Repository, and facial expression datasets show that our method outperforms previous methods in terms of the normalized mutual information.

本文言語英語
ホスト出版物のタイトルFoundations of Intelligent Systems - 22nd International Symposium, ISMIS 2015, Proceedings
編集者Floriana Esposito, Olivier Pivert, Stefano Ferilli, Zbigniew W. Raś, Mohand-Saïd Hacid
出版社Springer Verlag
ページ150-159
ページ数10
ISBN(印刷版)9783319252513
DOI
出版ステータス出版済み - 2015
イベント22nd International Symposium on Methodologies for Intelligent Systems, ISMIS 2015 - Lyon, フランス
継続期間: 10 21 201510 23 2015

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)
9384
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

その他

その他22nd International Symposium on Methodologies for Intelligent Systems, ISMIS 2015
国/地域フランス
CityLyon
Period10/21/1510/23/15

All Science Journal Classification (ASJC) codes

  • 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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