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Convergence of gradient method with momentum for two-layer feedforward neural networks
IEEE Transactions on Neural Networks
|March 29, 2006
Summary
This study introduces an adaptive momentum gradient method for two-layer neural networks, proving general convergence and error rate results without assuming a quadratic error function.
Area of Science:
- Machine Learning
- Artificial Intelligence
- Neural Networks
Background:
- Gradient-based optimization is fundamental to training artificial neural networks.
- Existing methods often rely on specific assumptions, such as quadratic error functions, limiting their generality.
Purpose of the Study:
- To analyze a gradient method with momentum for two-layer feedforward neural networks.
- To establish more general convergence and convergence rate results.
Main Methods:
- Utilizing a constant learning rate and an adaptive momentum factor.
- Applying mathematical analysis to prove weak and strong convergence properties.
Main Results:
- Demonstrated both weak and strong convergence for the proposed method.
- Established convergence rates for the error function and network weights.
- Results hold without the assumption of a quadratic error function, offering greater generality.
Conclusions:
- The proposed adaptive momentum gradient method offers improved generality for training two-layer feedforward neural networks.
- The established convergence rates provide theoretical insights into the learning process.
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