Abstract
In this paper, we propose a new type of recurrent neural network architecture in which each output unit is connected with itself and fully-connected with other output units and all hidden units. The proposed recurrent neural network differs from Jordan's and Elman's recurrent neural networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving the discrimination and generalization power. We also prove the convergence property of learning algorithm in the proposed recurrent neural network and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. Experimental results confirmed that the proposed recurrent neural network improves the discrimination and generalization power in recognizing spatial patterns.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Pattern Recognition |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 718-722 |
| Number of pages | 5 |
| Volume | 4 |
| ISBN (Print) | 081867282X, 9780818672828 |
| DOIs | |
| Publication status | Published - 1996 Jan 1 |
| Event | 13th International Conference on Pattern Recognition, ICPR 1996 - Vienna, Austria Duration: 1996 Aug 25 → 1996 Aug 29 |
Other
| Other | 13th International Conference on Pattern Recognition, ICPR 1996 |
|---|---|
| Country | Austria |
| City | Vienna |
| Period | 96/8/25 → 96/8/29 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
A new recurrent neural network architecture for pattern recognition. / Song, Hee Heon; Kang, Sun Mee; Lee, Seong Whan.
Proceedings - International Conference on Pattern Recognition. Vol. 4 Institute of Electrical and Electronics Engineers Inc., 1996. p. 718-722 547658.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A new recurrent neural network architecture for pattern recognition
AU - Song, Hee Heon
AU - Kang, Sun Mee
AU - Lee, Seong Whan
PY - 1996/1/1
Y1 - 1996/1/1
N2 - In this paper, we propose a new type of recurrent neural network architecture in which each output unit is connected with itself and fully-connected with other output units and all hidden units. The proposed recurrent neural network differs from Jordan's and Elman's recurrent neural networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving the discrimination and generalization power. We also prove the convergence property of learning algorithm in the proposed recurrent neural network and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. Experimental results confirmed that the proposed recurrent neural network improves the discrimination and generalization power in recognizing spatial patterns.
AB - In this paper, we propose a new type of recurrent neural network architecture in which each output unit is connected with itself and fully-connected with other output units and all hidden units. The proposed recurrent neural network differs from Jordan's and Elman's recurrent neural networks in view of functions and architectures because it was originally extended from the multilayer feedforward neural network for improving the discrimination and generalization power. We also prove the convergence property of learning algorithm in the proposed recurrent neural network and analyze the performance of the proposed recurrent neural network by performing recognition experiments with the totally unconstrained handwritten numeral database of Concordia University of Canada. Experimental results confirmed that the proposed recurrent neural network improves the discrimination and generalization power in recognizing spatial patterns.
UR - http://www.scopus.com/inward/record.url?scp=0009054509&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0009054509&partnerID=8YFLogxK
U2 - 10.1109/ICPR.1996.547658
DO - 10.1109/ICPR.1996.547658
M3 - Conference contribution
AN - SCOPUS:0009054509
SN - 081867282X
SN - 9780818672828
VL - 4
SP - 718
EP - 722
BT - Proceedings - International Conference on Pattern Recognition
PB - Institute of Electrical and Electronics Engineers Inc.
ER -