A Parallel Perceptron network for classification with direct calculation of the weights optimizing error and margin

@article{Delgado2010APP,
  title={A Parallel Perceptron network for classification with direct calculation of the weights optimizing error and margin},
  author={Manuel Fern{\'a}ndez Delgado and Jorge Ribeiro and Eva Cernadas and Sen{\'e}n Barro},
  journal={The 2010 International Joint Conference on Neural Networks (IJCNN)},
  year={2010},
  pages={1-8}
}
The Parallel Perceptron (PP) [1] is a simple neural network which has been shown to be a universal approximator, and it can be trained using the Parallel Delta (P-Delta) rule. This rule tries to maximize the distance between the perceptron activations and their decision hyperplanes in order to increase its generalization ability, following the principles of the Statistical Learning Theory. In this paper we propose a closed-form analytical expression to calculate, without iterations, the PP… CONTINUE READING