Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Introduction to Artificial Neural Systems
Introduction to Artificial Neural Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
MLP in layer-wise form with applications to weight decay
Neural Computation
Classification ability of single hidden layer feedforward neural networks
IEEE Transactions on Neural Networks
Using quick decision tree algorithm to find better RBF networks
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Connectionist approaches for predicting mouse gene function from gene expression
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
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Multilayer perceptrons (MLP) has been proven to be very successful in many applications including classification. The activation function is the source of the MLP power. Careful selection of the activation function has a huge impact on the network performance. This paper gives a quantitative comparison of the four most commonly used activation functions, including the Gaussian RBF network, over ten real different datasets. Results show that the sigmoid activation function substantially outperforms the other activation functions. Also, using only the needed number of hidden units in the MLP, we improved its conversion time to be competitive with the RBF networks most of the time.