DocumentCode
1748888
Title
A hybrid model approach to generalization in sequence learning
Author
Spiegel, Rainer ; McLaren, IPL
Author_Institution
Dept. of Exp. Psychol., Cambridge Univ., UK
Volume
4
fYear
2001
fDate
2001
Firstpage
2393
Abstract
Both recurrent neural networks and humans are able to learn sequential information and generalize to sequences they have not experienced in training. However, they sometimes seem to differ in the way they perform generalization. A new hybrid model is introduced that relies on both a recurrent neural network and rules typically applied by human subjects
Keywords
generalisation (artificial intelligence); learning (artificial intelligence); recurrent neural nets; generalization; hybrid model; recurrent neural networks; sequence learning; Artificial intelligence; Backpropagation algorithms; Context modeling; Humans; Intelligent networks; Neural networks; Predictive models; Psychology; Recurrent neural networks; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.938741
Filename
938741
Link To Document