Characterization of the Sonar Signals Benchmark
Neural Processing Letters
Efficient adaptive learning for classification tasks with binary units
Neural Computation
Input Feature Extraction for Multilayered Perceptrons Using Supervised Principal Component Analysis
Neural Processing Letters
An Introduction to the Modeling of Neural Networks
An Introduction to the Modeling of Neural Networks
Perceptron Learning Revisited: The Sonar Targets Problem
Neural Processing Letters
The geometrical learning of binary neural networks
IEEE Transactions on Neural Networks
A Solution for the N-bit Parity Problem Using a Single Translated Multiplicative Neuron
Neural Processing Letters
Learning data structures with inherent complex logic: neurocognitive perspective
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
| Hi-index | 0.00 |
The N-dimensional parity problem is frequently a difficult classification task for Neural Networks. We found an expression for the minimum number of errors νf as function of N for this problem, performed by a perceptron. We verified this quantity experimentally for N=1,…,15 using an optimal train perceptron. With a constructive approach we solved the full N-dimensional parity problem using a minimal feedforward neural network with a single hidden layer of h=N units.