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Finding hidden-feature depending laws inside a data set and classifying it using Neural Network
[article]
2021
arXiv
pre-print
The logcosh loss function for neural networks has been developed to combine the advantage of the absolute error loss function of not overweighting outliers with the advantage of the mean square error of continuous derivative near the mean, which makes the last phase of learning easier. It is clear, and one experiences it soon, that in the case of clustered data, an artificial neural network with logcosh loss learns the bigger cluster rather than the mean of the two. Even more so, the ANN, when
arXiv:2101.10427v1
fatcat:hq5zxv3uejgl7fulgtb6trgswi