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Related Experiment Videos

Avoiding overfitting in multilayer perceptrons with feeling-of-knowing using self-organizing maps.

Kazushi Murakoshi1

  • 1Department of Knowledge-based Information Engineering, Toyohashi University of Technology, 1-1 Hibarigaoka, Tenpaku-cho, Toyohashi 441-8580, Japan. mura@tutkie.tut.ac.jp

Bio Systems
|March 3, 2005
PubMed
Summary

This study introduces a novel method to prevent overfitting in multilayer perceptron (MLP) training using self-organizing maps (SOMs) and a feeling-of-knowing (FOK) mechanism. The proposed approach significantly reduces errors on test data, enhancing model generalization.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Overfitting is a significant challenge in multilayer perceptron (MLP) training, leading to poor generalization on unseen data.
  • Current on-line learning methods for MLPs often struggle to effectively mitigate overfitting.
  • The feeling-of-knowing (FOK) phenomenon offers a potential mechanism for regulating learning processes.

Purpose of the Study:

  • To develop and evaluate a novel method for avoiding overfitting in on-line MLP training.
  • To investigate the efficacy of incorporating a feeling-of-knowing (FOK) mechanism, guided by self-organizing maps (SOMs), into MLP learning.
  • To improve the generalization performance of MLPs in on-line learning scenarios.

Main Methods:

  • Proposed a modified MLP architecture that integrates a FOK mechanism calculated using SOMs.

Related Experiment Videos

  • The learning process in the proposed MLP dynamically adjusts based on the computed FOK degree.
  • Employed standard MLP training protocols for comparative analysis.

    • Main Results:

    • The proposed MLP with FOK using SOMs demonstrated a significantly lower mean square error on the test set compared to conventional MLP methods.
    • The FOK-guided learning process effectively controlled the model's complexity and prevented overfitting.
    • Empirical results indicate superior performance and generalization capabilities of the novel approach.

    Conclusions:

    • The integration of FOK, calculated via SOMs, provides an effective strategy to combat overfitting in on-line MLP training.
    • The proposed method offers a promising alternative to existing techniques for improving the robustness and reliability of MLPs.
    • This research contributes to the development of more stable and generalizable artificial neural network models.