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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hee-Heon Song Sun-Mee Kang Seong-Whan Lee |
| Copyright Year | 1996 |
| Description | Author affiliation: Switching Service Sect., ETRI, Taejon, South Korea (Hee-Heon Song) |
| 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. |
| Starting Page | 718 |
| Ending Page | 722 |
| File Size | 482843 |
| Page Count | 5 |
| File Format | |
| ISBN | 081867282X |
| ISSN | 10514651 |
| DOI | 10.1109/ICPR.1996.547658 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1996-08-25 |
| Publisher Place | Austria |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Recurrent neural networks Pattern recognition Multi-layer neural network Neural networks Feedforward neural networks Convergence Performance analysis Algorithm design and analysis Handwriting recognition Spatial databases |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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