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| Content Provider | ACM Digital Library |
|---|---|
| Author | Ghose, Udayan Chandra, Pravin Sehrawat, Ruchi |
| Abstract | On ten learning tasks (5 function approximation and 5 real life regression problems), we compare the effciency and efficacy of using asymmetric or anti-symmetric activation functions in sigmoidal feedforward artificial neural network training and usage. The result obtained in the experiment allows us to conclude that for networks trained using the improved variant of the resilient backpropagation algorithm, the usage of asymmetric activation functions like the logistic or the log-sigmoid function should be preferred as compared to anti-symmetric function such that the two functions have the same derivative. |
| Starting Page | 1 |
| Ending Page | 4 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450342131 |
| DOI | 10.1145/2979779.2979802 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-08-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Symmetry of activation function Feedforward artificial neural networks Activation function Squashing function Transfer function |
| Content Type | Text |
| Resource Type | Article |
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