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Related Experiment Videos

OCReP: An Optimally Conditioned Regularization for pseudoinversion based neural training.

Rossella Cancelliere1, Mario Gai2, Patrick Gallinari3

  • 1University of Turin, Department of Computer Sciences, C.so Svizzera 185, 10149 Torino, Italy.

Neural Networks : the Official Journal of the International Neural Network Society
|August 31, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a stable method for training neural networks using Tikhonov regularization. The approach identifies optimal regularization parameters, enhancing predictive performance and reducing computational costs in regression and classification tasks.

Keywords:
Condition numberNumerical instabilityPseudoinversionRegularization parameter

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Area of Science:

  • Machine Learning
  • Numerical Analysis

Background:

  • Single hidden layer neural network training via pseudoinversion can suffer from numerical instability.
  • Regularization techniques, particularly Tikhonov regularization, are effective in mitigating these issues.

Purpose of the Study:

  • To introduce a matricial reformulation for Tikhonov regularization in neural network training.
  • To utilize the condition number as a diagnostic tool for instability.
  • To identify an optimal regularization parameter for stability and performance.

Main Methods:

  • Developed a matricial reformulation of the pseudoinversion training problem within the Tikhonov regularization framework.
  • Imposed well-conditioning requirements on relevant matrices to analyze stability.
  • Determined an optimal regularization parameter analytically.

Main Results:

  • The condition number effectively diagnoses numerical instability.
  • Analytical determination of the regularization parameter provides stability and optimizes performance.
  • The proposed method demonstrates effectiveness in both regression and classification tasks, often outperforming reference methods.
  • Significantly reduced computational load compared to other techniques.

Conclusions:

  • The Tikhonov regularization approach offers a stable and computationally efficient method for training neural networks.
  • Analytical parameter determination enhances predictive accuracy and generalization.
  • This method provides a valuable alternative for stable and performant neural network training.