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Neural Networks for Predicting Conditional Probability Densities: Improved Training Scheme Combining EM and RVFL.

John G. Taylor1, Dirk Husmeier

  • 1Department of Mathematics, King's College London, UK

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2003
PubMed
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This study accelerates neural network training for predicting conditional probability densities using a Random Vector Functional Link (RVFL) network concept. This significantly improves generalization performance and reduces training time, enabling ensemble model development.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Statistics

Background:

  • Neural networks for conditional probability density prediction often require complex architectures.
  • These complex models typically result in lengthy training durations.
  • Existing methods face challenges in balancing model complexity and training efficiency.

Purpose of the Study:

  • To accelerate the training process for neural networks predicting conditional probability densities.
  • To investigate the effectiveness of the Random Vector Functional Link (RVFL) concept for this task.
  • To enhance the generalization performance of predictive models.

Main Methods:

  • Adoption of the Random Vector Functional Link (RVFL) network concept.
  • Constraining a subset of network parameters to random initial values.

Related Experiment Videos

  • Application of the Expectation-Maximization (EM) algorithm facilitated by parameter constraints.
  • Training an ensemble of networks with accelerated computational efficiency.

    • Main Results:

    • Training time acceleration by approximately two orders of magnitude compared to standard methods.
    • Enabling the training of multiple network models within equivalent computational budgets.
    • Demonstrated significant improvements in the generalization performance of the trained models.
    • Validation through simulations indicating the efficacy of the proposed approach.

    Conclusions:

    • The RVFL concept, combined with EM-algorithm applicability, offers a substantial speedup in training neural networks for density prediction.
    • This accelerated training facilitates the development of more robust and accurate predictive models through ensembling.
    • The method presents a viable strategy for improving predictive accuracy and computational efficiency in complex modeling tasks.