… In this paper, we propose the use of a threelayer feedforward neural network to select those
input attributes that are most useful for discriminating classes in a given set of input patterns. …

… to select the optimum inputs are known as feature selection techniques. Their use in the
context of arti cial neural networks … forward selection and the genetic algorithm, were applied. …

… selection techniques We compare the proposed neural network based feature selection …
which banks on different concept, namely, the neural-network feature selector (NNFS) based on …

… neural networks for pattern classification, with its performance primarily determined by its
architecture selection. … The first, unsupervised stage is to select the center positions and widths …

… The selection of an optimum chromosome set is referred to in this paper as feature selection.
Quantitative comparisons of four of the most commonly used ANN activation functions …

… error functions in multi-layer feedforward neural networks used for … neural networks that
have been trained with the usual mean square error function, the mean absolute mor function, …

… In neural networks, feature selection has been studied for the … a review of neural network
approaches to feature selection. We … developed specifically for neural networks. Representative …

… neural network-based methods to perform variable selection. … a term to the cost function used
to train a neural network. In the … regularization approach allows to select efficient subsets of …

… neural networks is the selection of the proper size and topology of the networks. The problem
is even … Let us select the FCC neural network architecture and apply the NBN algorithm for …

… The weight initialization and the activation function of deep neural networks have a crucial
impact on the performance of the training procedure. An inappropriate selection can lead to …