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Related Experiment Videos

Transfer Extreme Learning Machine with Output Weight Alignment.

Shaofei Zang1, Yuhu Cheng2, Xuesong Wang2

  • 1Department of Information Engineering College, Henan University of Science and Technology, Luoyang 471000, China.

Computational Intelligence and Neuroscience
|February 25, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces the Transfer Extreme Learning Machine with Output Weight Alignment (TELM-OWA), a novel approach to improve machine learning model performance with limited data. TELM-OWA enhances knowledge transferability for better pattern recognition and classification tasks.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Science

Background:

  • Extreme Learning Machines (ELM) offer fast computation but degrade with insufficient labeled training data.
  • Transfer learning leverages source domain data to improve target domain learning, especially when target data is scarce.

Purpose of the Study:

  • To propose a supervised Extreme Learning Machine with enhanced knowledge transferability, termed Transfer Extreme Learning Machine with Output Weight Alignment (TELM-OWA).
  • To address the performance decline of ELMs due to limited labeled samples by enabling cross-domain knowledge transfer.

Main Methods:

  • TELM-OWA reduces domain distribution differences by aligning ELM output weight matrices from source and target domains.
  • It incorporates inter-domain ELM output weight matrix approximation into the objective function for improved knowledge transfer.
  • The objective function is reformulated as a least squares problem, enabling efficient solution within a standard ELM framework.

Main Results:

  • The proposed TELM-OWA algorithm demonstrated competitive performance across 16 image and 6 text classification datasets.
  • Effectiveness was validated against other ELM models and existing transfer learning approaches.

Conclusions:

  • TELM-OWA effectively enhances knowledge transferability in Extreme Learning Machines, outperforming traditional ELMs and other transfer learning methods.
  • The method provides a robust solution for machine learning tasks with limited labeled data, improving classification accuracy in both image and text domains.