Self-organizing maps
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Circular nodes in neural networks
Neural Computation
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Circular backpropagation networks for classification
IEEE Transactions on Neural Networks
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Multilayer Perceptrons (MLPs) use scalar products to compute weighted activation of neurons providing decision borders using combinations of soft hyperplanes. The weighted fun-in activation function may be replaced by a distance function between the inputs and the weights, offering a natural generalization of the standard MLP model. Non-Euclidean distance functions may also be introduced by normalization of the input vectors into an extended feature space. Both approaches influence the shapes of decision borders dramatically. An illustrative example showing these changes is provided.