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 |
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ホスト出版物のタイトル | 91 IEEE Int Jt Conf Neural Networks IJCNN 91 |
Place of Publication | Piscataway, 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月 18 → 1991 11月 21 |
Other
Other | 1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 |
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City | Singapore, Singapore |
Period | 91/11/18 → 91/11/21 |
ASJC Scopus subject areas
- 工学(全般)