DocumentCode
288438
Title
One-shot algorithm for temporal sequences
Author
Demura, Kosei ; Kajiura, Masahiro ; Anzai, Yuichiro
Author_Institution
Dept. of Comput. Sci., Keio Univ., Yokohama, Japan
Volume
2
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
868
Abstract
Recurrent SOLAR (Supervised One-shot Learning Algorithm for Real number inputs) requires only a single presentation of an analog training set for learning temporal sequences. Recurrent SOLAR does not use the gradient decent algorithm, so it has no local minima problem, no topological problem and extraordinary speed-up compared to the algorithms based on the gradient decent method
Keywords
feedforward neural nets; learning (artificial intelligence); recurrent neural nets; sequences; analog training set; recurrent SOLAR; supervised one-shot learning algorithm for real number inputs; temporal sequences; Application specific processors; Computer science; Context modeling; Hazards; Large-scale systems; Network topology; Spatiotemporal phenomena; Supervised learning; Symmetric matrices; Tiles;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374294
Filename
374294
Link To Document