A kernel level composition of multiple local classifiers for nonlinear classification

Weite Li, Bo Zhou, Takayuki Furuzuki

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Kernel functions based machine learning algorithms have been extensively studied over the past decades with successful applications in a variety of real-world tasks. In this paper, we formulate a kernel level composition method to embed multiple local classifiers (kernels) into one kernel function, so as to obtain a more flexible data-dependent kernel. Since such composite kernels are composed by multiple local classifiers interpolated with several localizing gating functions, a specific learning process is also introduced in this paper to pre-determine their parameters. Experimental results are provided to validate two major perspectives of this paper. Firstly, the introduced learning process is effective to detect local information, which is essential for the parameter pre-determination of the localizing gating functions. Secondly, the proposed composite kernel has a capacity to improve classification performance.

本文言語English
ホスト出版物のタイトル2016 International Joint Conference on Neural Networks, IJCNN 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3845-3850
ページ数6
2016-October
ISBN(電子版)9781509006199
DOI
出版ステータスPublished - 2016 10 31
イベント2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
継続期間: 2016 7 242016 7 29

Other

Other2016 International Joint Conference on Neural Networks, IJCNN 2016
CountryCanada
CityVancouver
Period16/7/2416/7/29

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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