Handbook of Neural Computation
Handbook of Neural Computation
Training Higher Order Gaussian Synapses
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
A Hyperbolic Multilayer Perceptron
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 2 - Volume 2
On the effects of dimensionality on data analysis with neural networks
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
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This paper proposes a neural network structure as well as an adaptation of the backpropagation algorithm for its training that provides a way to consider multidimensional information directly in its original space. Traditionally, when inputting multidimensional information to artificial neural networks, its components are fed individually through different inputs and basically processed separately throughout the network. In the present structure, the multidimensional information, in the form of vectors is processed as such in the network, thus preserving in a simple way all the multidimensional neighbourhood relationships. The projection into the dimensionality of the output space is also carried out within the network. This procedure allows for a simpler processing of multidimensional signals such as multi or hyperspectral cubes as used in remote sensing or colour signals in images, which is the example we present as a test for the algorithm.