In order to train a network with a hyperbolic tangent output activation function, the targets were transformed to the range between -1 and 1 with a modified Z-score normalization (see details in Appendix B). During the training we used a quadratic cost function and trained with a batch size...
Therefore, it is not required to train different networks for different benchmarks. One single network can take care of it. Also, any underlying configuration between two chosen signal benchmarks can be inferred more precisely with the help of parametric DNN. A detailed discussion of p-DNN ...
The training set is used to train the classifier, while the testing set is used to test the predictions of the algorithm. This splitting of the dataset is, however, not the procedure we followed here. Since random forests employ bagging, a single data point is used only in training of a...