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This study introduces a novel training algorithm using multiple online sequential Random Vector Functional Link (OS-RVFL) networks for efficient large-scale dataset processing. The method significantly reduces training time and improves classification accuracy through parallel processing and a frequency criterion.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Science

Background:

  • Randomization-based neural networks, including Random Vector Functional Link (RVFL) networks, are valued for their simplicity and generalization.
  • Real-world applications necessitate online learning algorithms capable of updating models with new data, especially for large-scale datasets.
  • Existing online sequential algorithms often involve distinct initial and sequential learning phases.

Purpose of the Study:

  • To propose a novel training algorithm for large-scale databases using multiple online sequential Random Vector Functional Link (OS-RVFL) networks.
  • To enhance computational efficiency and classification accuracy in machine learning tasks involving massive datasets.
  • To develop a predictive model for the total training time of the proposed algorithm.

Main Methods:

  • A shared memory architecture distributes training data across multiple OS-RVFL networks trained in parallel using p threads.
  • Test data samples are classified by each trained OS-RVFL network.
  • A frequency criterion is applied to the outputs of individual networks for final classification.
  • An equation is derived to predict the total training time based on initial learning and time scaling factors.

Main Results:

  • The proposed algorithm demonstrates a significant reduction in training time due to data distribution and parallel processing.
  • Classification accuracy is improved by the implementation of the frequency criterion for final decision-making.
  • The derived equation provides a reasonable prediction of the overall training duration.

Conclusions:

  • The multiple OS-RVFL network approach offers an efficient solution for training on large-scale datasets.
  • Parallel processing and a frequency-based classification strategy enhance both speed and accuracy.
  • The predictive model for training time aids in resource management and performance estimation.