Comprehensibility maximization and humanly comprehensible representations
From MaRDI portal
Publication:2862325
DOI10.1080/03081079.2011.643471zbMath1274.93003OpenAlexW2148132233MaRDI QIDQ2862325
Publication date: 14 November 2013
Published in: International Journal of General Systems (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03081079.2011.643471
Learning and adaptive systems in artificial intelligence (68T05) General systems (93A10) Measures of information, entropy (94A17)
Uses Software
Cites Work
- Generating rules with predicates, terms and variables from the pruned neural networks
- V4 receptive field dynamics as predicted by a systems-level model of visual attention using feedback from the frontal eye field
- Attention in natural scenes: neurophysiological and computational bases
- Attention as a controller
- Advanced visualization of self-organizing maps with vector fields
- Vector quantization using information theoretic concepts
- SOM-based data visualization methods
- A Penalty-Function Approach for Pruning Feedforward Neural Networks
- 10.1162/153244303322753616
- 10.1162/153244303322753742
- Learning from examples with information theoretic criteria
- Symbolic knowledge extraction from trained neural networks: A sound approach
- Rule extraction from expert heuristics: A comparative study of rough sets with neural networks and ID3
This page was built for publication: Comprehensibility maximization and humanly comprehensible representations