Kohonen feature maps as a supervised learning machine

Hiroyuki Ichiki, Masafumi Hagiwara, Masao Nakagawa

研究成果: Conference contribution

31 被引用数 (Scopus)

抄録

Kohonen Feature Maps as a Supervised Learning Machine are proposed and discussed. The proposed models adopt the supervised learning without modifying the basic learning algorithm. Therefore they behave as a supervised learning machine, which can learnt input-output functions in addition to the characteristics of the conventional method that is to say structuring a pattern recognition after preprocessing by the Kohenen Feature Map. In addition, the proposed models don't distinguish the input vectors from the desired vectors because they regard them as the same kind of vectors. That enables their bidirectional associations. And, we simulated several examples in order to compare with the conventional supervised learning machines. The results indicate the effectiveness of the proposed models. For example, the property to noise, capacity storage and so on. We confirmed that the proposed models had better characteristics than the conventional models (Back propagation network for a pattern recognition and BAM for an associative memory).

本文言語English
ホスト出版物のタイトル1993 IEEE International Conference on Neural Networks
出版社Publ by IEEE
ページ1944-1948
ページ数5
ISBN(印刷版)0780312007
出版ステータスPublished - 1993 1月 1
イベント1993 IEEE International Conference on Neural Networks - San Francisco, California, USA
継続期間: 1993 3月 281993 4月 1

出版物シリーズ

名前1993 IEEE International Conference on Neural Networks

Other

Other1993 IEEE International Conference on Neural Networks
CitySan Francisco, California, USA
Period93/3/2893/4/1

ASJC Scopus subject areas

  • 工学(全般)
  • 制御およびシステム工学
  • ソフトウェア
  • 人工知能

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