Abstract
In this paper, Quick Learning algorithm for Multidirectional Associative Memories (MAMs) is proposed. Owing to the Quick Learning algorithm, not only the storage capacity of the MAMs can be improved, but also the recall of all training data can be guaranteed. In addition, several important characteristics of the MAMs such as the relation between the required learning epochs and the number of layers and the relation between the noise reduction effect and the number of layers are introduced.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Publisher | IEEE |
| Pages | 1949-1954 |
| Number of pages | 6 |
| Volume | 4 |
| Publication status | Published - 1995 |
| Event | Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust Duration: 1995 Nov 27 → 1995 Dec 1 |
Other
| Other | Proceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) |
|---|---|
| City | Perth, Aust |
| Period | 95/11/27 → 95/12/1 |
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ASJC Scopus subject areas
- Software
Cite this
Quick learning for multidirectional associative memories. / Hattori, Motonobu; Hagiwara, Masafumi.
IEEE International Conference on Neural Networks - Conference Proceedings. Vol. 4 IEEE, 1995. p. 1949-1954.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Quick learning for multidirectional associative memories
AU - Hattori, Motonobu
AU - Hagiwara, Masafumi
PY - 1995
Y1 - 1995
N2 - In this paper, Quick Learning algorithm for Multidirectional Associative Memories (MAMs) is proposed. Owing to the Quick Learning algorithm, not only the storage capacity of the MAMs can be improved, but also the recall of all training data can be guaranteed. In addition, several important characteristics of the MAMs such as the relation between the required learning epochs and the number of layers and the relation between the noise reduction effect and the number of layers are introduced.
AB - In this paper, Quick Learning algorithm for Multidirectional Associative Memories (MAMs) is proposed. Owing to the Quick Learning algorithm, not only the storage capacity of the MAMs can be improved, but also the recall of all training data can be guaranteed. In addition, several important characteristics of the MAMs such as the relation between the required learning epochs and the number of layers and the relation between the noise reduction effect and the number of layers are introduced.
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UR - http://www.scopus.com/inward/citedby.url?scp=0029514614&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0029514614
VL - 4
SP - 1949
EP - 1954
BT - IEEE International Conference on Neural Networks - Conference Proceedings
PB - IEEE
ER -