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A fast and accurate online sequential learning algorithm for feedforward networks.

Nan-Ying Liang1, Guang-Bin Huang, P Saratchandran

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.

IEEE Transactions on Neural Networks
|November 30, 2006
PubMed
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This study introduces the online sequential extreme learning machine (OS-ELM), a fast and efficient algorithm for single hidden layer feedforward networks. OS-ELM demonstrates superior performance in sequential learning tasks compared to existing methods.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Neural Networks

Background:

  • Single hidden layer feedforward networks (SLFNs) are foundational in machine learning.
  • Existing batch learning methods like ELM offer speed but lack sequential adaptability.
  • Sequential learning algorithms are crucial for real-time data processing.

Purpose of the Study:

  • To develop a unified online sequential learning algorithm for SLFNs.
  • To introduce the online sequential extreme learning machine (OS-ELM) framework.
  • To enable learning from data arriving one-by-one or in chunks.

Main Methods:

  • Developed the online sequential extreme learning machine (OS-ELM) algorithm.
  • OS-ELM supports both additive and radial basis function (RBF) hidden nodes.

Related Experiment Videos

  • Randomly selected hidden node parameters and analytically determined output weights.

    • Main Results:

    • OS-ELM processes data sequentially (one-by-one or chunk-by-chunk).
    • The algorithm exhibits faster training speeds compared to other sequential learning methods.
    • OS-ELM achieves better generalization performance across regression, classification, and time series prediction tasks.

    Conclusions:

    • OS-ELM provides an efficient and effective approach for online sequential learning in SLFNs.
    • The algorithm requires minimal parameter tuning, simplifying its application.
    • OS-ELM represents a significant advancement over traditional sequential learning techniques.