Artificial Neural Network Generalization and Simplification via Pruning

Artificial neural networks (ANNs) are well known for their classification abilities. Although choosing hyperparameters such as neuron layer count and size can be a quite tedious task. Pruning approaches assume that a sufficiently large ANN has already been trained and can be simplified with acceptable classification accuracy loss. The current paper presents a node pruning algorithm and gives experimental results for pruned network accuracy rates versus their non-pruned counterparts