Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94) 1994
DOI: 10.1109/icnn.1994.374204
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Dynamic adaptation of the error surface for the acceleration of the training of neural networks

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Cited by 3 publications
(5 citation statements)
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“…Figure 2 shows the average number of neurons which undergo the opposite transformation per epoch for the cancer dataset. As described above, the behavior is essentially governed by Equation (14). As such, a similar behavior was seen for all datasets.…”
Section: A Function Call Overhead Analysissupporting
confidence: 64%
See 2 more Smart Citations
“…Figure 2 shows the average number of neurons which undergo the opposite transformation per epoch for the cancer dataset. As described above, the behavior is essentially governed by Equation (14). As such, a similar behavior was seen for all datasets.…”
Section: A Function Call Overhead Analysissupporting
confidence: 64%
“…V( i) is some userdefined distribution or probability decision rule and U (0, 1) is a uniformly distributed real number, and (14) and represents the neurons which have the last two successive outputs either above or below <p (0).…”
Section: * E [Lcur I Cur]mentioning
confidence: 99%
See 1 more Smart Citation
“…Dynamically adjusting transfer function parameters, and thus modifying the error surface and input-output mapping has been investigated, for example see [11], [12]. Similarly, many alternative transfer functions have been proposed, refer to [8] for a survey.…”
Section: Opposite Networkmentioning
confidence: 99%
“…Investigating the impact of transfer functions on the error surface has also been researched recently [8], [9], [10]. Also, adaptive transfer functions have been considered as a possible means to help alleviate ill-conditioning and improve accuracy and training times [11], [12], [13], [14].…”
Section: Introductionmentioning
confidence: 99%