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Training a Hyperdimensional Computing Classifier Using a Threshold on Its Confidence.

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Summary
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This study enhances hyperdimensional computing (HDC) training by considering low-confidence correct predictions, improving accuracy and confidence for machine learning on edge devices.

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Area of Science:

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

Background:

  • Hyperdimensional computing (HDC) offers efficient machine learning for resource-constrained devices like wearables.
  • Current HDC training primarily focuses on misclassified samples, potentially missing opportunities for model refinement.
  • HDC's computational efficiency makes it suitable for edge computing and Internet-of-Things (IoT) applications.

Purpose of the Study:

  • To improve Hyperdimensional Computing (HDC) classification accuracy and confidence.
  • To introduce an extended training procedure that incorporates low-confidence correctly classified samples.
  • To evaluate the effectiveness of the proposed training method across diverse datasets.

Main Methods:

  • An extended HDC training procedure was developed, incorporating samples classified with low confidence.
  • A tunable confidence threshold was introduced to optimize classification accuracy for different datasets.
  • The proposed method was evaluated on UCIHAR, CTG, ISOLET, and HAND datasets.

Main Results:

  • The extended training procedure consistently improved classification performance compared to baseline HDC methods.
  • Performance gains were observed across various confidence threshold values on tested datasets.
  • The model demonstrated increased confidence in correctly classifying samples after the enhanced training.

Conclusions:

  • The proposed training extension enhances HDC model accuracy and prediction confidence.
  • Incorporating low-confidence correct predictions is a viable strategy for improving HDC performance.
  • This approach offers a more robust and reliable HDC classifier for edge AI applications.