| Título |
Geometric Associative Processing Applied to Pattern Classification |
| Tipo |
Congreso |
| Sub-tipo |
SCOPUS |
| Descripción |
Lecture Notes in Computer Science; 6th International Symposium on Neural Networks, |
| Resumen |
Associative memories (AM’s) have been extensively used during the last 40 years for pattern classification and pattern restoration. In this paper Conformal Geometric Algebra (CGA) is used to develop a new associative memory (AM). The proposed AM makes use of CGA and quadratic programming to store associations among patterns and their respective classes. An unknown pattern is classified by applying an inner product between the pattern and the build AM. Numerical and real examples are presented to show the potential of the proposal. |
| Observaciones |
Advances in Neural Networks – ISNN 2009; (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Code 77071 |
| Lugar |
Wuhan |
| País |
China |
| No. de páginas |
977-985 |
| Vol. / Cap. |
5552 |
| Inicio |
2009-05-26 |
| Fin |
2009-05-29 |
| ISBN/ISSN |
978-3-642-01509-0 |
