Hyperdimensional computing (HDC) has become popular for light-weight and energy-efficient machine learning, suitable for wearable Internet-of-Things devices and near-sensor or on-device processing. HDC is computationally less complex than traditional deep learning algorithms and achieves moderate to good classification performance. This letter proposes to extend the training procedure in HDC by taking into account not only wrongly classified samples but also samples that are correctly classified by the HDC model but with low confidence. We introduce a confidence threshold that can be tuned for each data set to achieve the best classification accuracy. The proposed training procedure is tested on UCIHAR, CTG, ISOLET, and HAND data sets for which the performance consistently improves compared to the baseline across a range of confidence threshold values. The extended training procedure also results in a shift toward higher confidence values of the correctly classified samples, making the classifier not only more accurate but also more confident about its predictions.

超维计算 (HDC) 因轻量级和节能的机器学习而变得流行，适用于可穿戴物联网设备以及近传感器或设备上处理。HDC 的计算复杂度低于传统的深度学习算法，并且可以实现中等至良好的分类性能。这封信建议扩展 HDC 中的训练过程，不仅考虑错误分类的样本，还考虑 HDC 模型正确分类但置信度较低的样本。我们引入了一个置信度阈值，可以针对每个数据集进行调整，以实现最佳的分类精度。所提出的训练程序在 UCIHAR、CTG、ISOLET 和 HAND 数据集上进行了测试，与一系列置信阈值的基线相比，其性能持续提高。扩展的训练过程还会导致正确分类的样本的置信度值更高，从而使分类器不仅更准确，而且对其预测更有信心。