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MLP in layer-wise form with applications to weight decay.
1Department of Mathematical Information Technology, University of Jyväskylä, P.O.Box 35 (Agora), FIN-40351 Jyväskylä, Finland. tka@mit.jyu.fi
Neural Computation
|May 22, 2002
Summary
A new calculus simplifies sensitivity analysis for feedforward Multi-Layer Perceptron (MLP) networks. This method aids in understanding the least-means-squares learning problem and comparing weight decay techniques.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Neural Networks
Background:
- Sensitivity analysis is crucial for understanding neural network behavior.
- Existing methods for feedforward Multi-Layer Perceptron (MLP) networks can be complex.
- Layer-wise analysis offers a structured approach to sensitivity.
Purpose of the Study:
- To present a simple and general calculus for sensitivity analysis of feedforward MLP networks.
- To explore consequences for the least-means-squares learning problem based on local optimality.
- To facilitate the comparison of different weight decay techniques.
Main Methods:
- Developed a layer-wise calculus for sensitivity analysis.
- Utilized local optimality conditions to derive consequences for learning.
- Conducted numerical experiments to compare weight decay methods.
Main Results:
- The proposed calculus provides a straightforward method for sensitivity analysis.
- Insights into the least-means-squares learning problem were gained.
- Empirical comparison of weight decay techniques was successfully performed.
Conclusions:
- The layer-wise calculus is effective for feedforward MLP sensitivity analysis.
- The approach offers valuable insights into neural network learning dynamics.
- This work supports the development and comparison of regularization techniques.