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Related Experiment Videos

On objective function, regularizer, and prediction error of a learning algorithm for dealing with multiplicative

John Pui-Fai Sum1, Chi-Sing Leung, Kevin I-J Ho

  • 1Department of Electronic Engineering, City University of Hong Kong, Hong Kong. pfsum@dragon.nchu.edu.tw

IEEE Transactions on Neural Networks
|December 26, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a new objective function to train functional link networks, enhancing their tolerance to multiplicative weight noise. The findings reveal this function is similar to weight decay, improving fault tolerance in neural networks.

Related Experiment Videos

Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Artificial Intelligence

Background:

  • Neural networks, including functional link networks (FLNs) and radial basis function (RBF) networks, are susceptible to multiplicative weight noise.
  • Existing regularization techniques can improve fault tolerance, but a specific objective function for FLNs with multiplicative noise is needed.

Purpose of the Study:

  • To present an objective function for training functional link networks that specifically addresses multiplicative weight noise.
  • To analyze the relationship between the proposed regularizer and existing methods like weight decay.
  • To develop and verify a learning algorithm for FLNs under multiplicative weight noise.

Main Methods:

  • Development of a novel objective function comprising a mean square training error term and a regularizer term.
  • Theoretical analysis to demonstrate the equivalence of the derived regularizer to weight decay under certain conditions.
  • Derivation of a learning algorithm tailored for FLNs with multiplicative weight noise.
  • Empirical validation using simulated experiments on artificial datasets and a real-world application.

Main Results:

  • The proposed objective function effectively trains functional link networks to tolerate multiplicative weight noise.
  • The derived regularizer is shown to be equivalent to weight decay, explaining its effectiveness in improving fault tolerance for RBF networks.
  • The learning algorithm derived from the objective function is simple and effective.
  • Experimental results confirm the theoretical findings, demonstrating reduced mean prediction error in trained networks.

Conclusions:

  • The novel objective function provides a robust method for enhancing the fault tolerance of functional link networks against multiplicative weight noise.
  • The study bridges the understanding between regularization techniques, highlighting the role of weight decay in noise tolerance.
  • The developed learning algorithm and validated approach offer practical improvements for deploying neural networks in noisy environments.