Artificial Neural Networks Generalization and Simplification via Pruning

Artificial neural networks (ANN) are well known for their classification abilities although, but choosing hyper parameters such as neuron layers count and sizes can be quite tedious task. Pruning approaches assume that sufficiently large ANN is already trained and can be simplified with acceptable classification accuracy loss. Current paper presents nodes pruning algorithm and gives experimental results for pruned networks accuracy rates versus their non-pruned counterparts