Injection of external information to feature maps of multiply descent cost competitive learning

Yasuo Matsuyama, Yasushi Kurosawa

研究成果: Conference contribution

抄録

Multiple descent cost competitive learning simultaneously generates two types of feature maps by self-organization. One is a grouped pattern of atomic data elements; the other is a geometric structure on the set of neural weight vectors. In the case of images, the grouped pattern is a set of nonoverlapping quadrilaterals. Each quadrilateral is associated with a neural weight vector, i.e., an image patch. Then, control of the grouped pattern based on external intelligence creates new images. By this method, generation of new emotional features on facial images is attempted. Thus, the feature map of the multiple descent cost competitive learning is not used for recognition but is utilized for creation of new patterns by incorporating additional information.

本文言語English
ホスト出版物のタイトル91 IEEE Int Jt Conf Neural Networks IJCNN 91
Place of PublicationPiscataway, NJ, United States
出版社Publ by IEEE
ページ994-1000
ページ数7
ISBN(印刷版)0780302273
出版ステータスPublished - 1991
外部発表はい
イベント1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore
継続期間: 1991 11 181991 11 21

Other

Other1991 IEEE International Joint Conference on Neural Networks - IJCNN '91
CitySingapore, Singapore
Period91/11/1891/11/21

ASJC Scopus subject areas

  • Engineering(all)

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