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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Uda, N. Yamamoto, A. Kimura, F. Tsuruoka, S. Miyake, Y. |
| Copyright Year | 1993 |
| Description | Author affiliation: Inf. Process. Center, Mie Univ., Tsu, Japan (Uda, N.) |
| Abstract | A sigmoid function has been utilized for the input/output functions of the backpropagation type neural networks. It, however, has a local minimum problem; if the output of the sigmoid function becomes 0 or 1, no further learning occurs even if there are errors between teaching inputs and outputs of the output unit. The offset method of applying some offset values to the intermediate layer cells is thought to be effective in solving the local minimum problem. In this paper, we propose two formulations of offset function: the linearly decremental offset function that decrements offset value as iteration of the learning process increases, and the logarithmic error offset function that varies offset values according as logarithm of output errors. The performance of these methods are evaluated by recognition test of handwriting. |
| Starting Page | 540 |
| Ending Page | 544 |
| File Size | 538892 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780314212 |
| DOI | 10.1109/IJCNN.1993.713972 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 1993-10-25 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Neural networks Education Equations Handwriting recognition Testing Character recognition Information processing Information systems Laboratories Stacking |
| Content Type | Text |
| Resource Type | Article |
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