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Sensitivity analysis of single hidden-layer neural networks with threshold functions
IEEE Transactions on Neural Networks
|January 1, 1995
Summary
Neural networks used in pattern recognition are sensitive to errors. This study quantifies sensitivity in single hidden-layer networks, finding trained networks differ significantly from random ones.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Pattern Recognition
Background:
- Neural networks are susceptible to weight perturbations and input errors, impacting pattern recognition accuracy.
- Understanding this sensitivity is crucial for reliable deployment of neural network models.
Purpose of the Study:
- To analyze the sensitivity of single hidden-layer neural networks with threshold functions to weight perturbations and input errors.
- To derive a mathematical function quantifying the probability of inversion error.
Main Methods:
- Mathematical derivation of inversion error probability based on trained weights, input patterns, and error variances.
- Simulation using the Madaline network for handwritten digit recognition to validate derived results.
Main Results:
- The probability of inversion error is expressed as a function of network parameters and error characteristics.
- Simulations confirmed that trained networks exhibit different sensitivity levels compared to networks with random weights.
Conclusions:
- The sensitivity of neural networks to errors is highly dependent on their trained state, not just random initialization.
- This analysis provides a quantitative framework for assessing the robustness of neural networks in pattern recognition tasks.
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