Artificial Neural Network Generalization and Simplification via Pruning/ Mākslīgo neironu tīklu vispārināšana un vienkāršošana, izmantojot samazināšanu/ Обобщение и упрощение искусственных нейронных сетей через урезание
Year: 2014
Type: article
Abstract: Artificial neural networks (ANNs) are well known for their classification abilities. Although choosing hyper- parameters 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.
Authors: Andrey Bondarenko, Arkady Borisov
Institution: Riga Technical University
Language: Russian
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2280753833