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

Yasuo Matsuyama, Yasushi Kurosawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication91 IEEE Int Jt Conf Neural Networks IJCNN 91
Place of PublicationPiscataway, NJ, United States
PublisherPubl by IEEE
Pages994-1000
Number of pages7
ISBN (Print)0780302273
Publication statusPublished - 1991
Externally publishedYes
Event1991 IEEE International Joint Conference on Neural Networks - IJCNN '91 - Singapore, Singapore
Duration: 1991 Nov 181991 Nov 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)

Fingerprint Dive into the research topics of 'Injection of external information to feature maps of multiply descent cost competitive learning'. Together they form a unique fingerprint.

  • Cite this

    Matsuyama, Y., & Kurosawa, Y. (1991). Injection of external information to feature maps of multiply descent cost competitive learning. In 91 IEEE Int Jt Conf Neural Networks IJCNN 91 (pp. 994-1000). Publ by IEEE.