Multilayer self-organizing category formation networks

Masao Sakai, Noriyasu Homma, Yosuke Koyanaka, Kenichi Abe

研究成果: Conference contribution

抄録

In this paper, we develop a new multilayer neural model that forms categories of inputs for some practical applications such as pattern recognition, learning, image processing, and trend analysis. An essential core of the model is to use a novel vector representation of concepts that compose an input in a multi-level informational hierarchy that makes the model possess category formation ability from incomplete observation of the input. Simulation results demonstrate the usefulness of the model for a facial image recognition task, even if it is carried out under an incremental and unsupervised learning environment. In addition, we evaluate the adequacy and efficiency of formed categories by using principal component analysis.

本文言語English
ホスト出版物のタイトルProceedings of the SICE Annual Conference
ページ2895-2900
ページ数6
出版ステータスPublished - 2005
イベントSICE Annual Conference 2005 - Okayama, Japan
継続期間: 2005 8 82005 8 10

Other

OtherSICE Annual Conference 2005
国/地域Japan
CityOkayama
Period05/8/805/8/10

ASJC Scopus subject areas

  • 工学(全般)

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