Hyperdimensional computing (HDC) has become popular for light-weight and energy-efficient machine learning, suitable for wearable Internet-of-Things (IoT) 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 article 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. As such, a confidence threshold is introduced that can be tuned for each dataset to achieve the best classification accuracy. The proposed training procedure is tested on UCIHAR, CTG, ISOLET and HAND dataset 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 towards higher confidence values of the correctly classified samples making the classifier not only more accurate but also more confident about its predictions.

翻译：超维计算（HDC）因其轻量级和高效节能的机器学习特性而广受欢迎，适用于可穿戴物联网设备以及近传感器或片上处理场景。相较于传统深度学习算法，HDC计算复杂度更低，并能实现中等至良好的分类性能。本文提出扩展HDC训练流程，不仅考虑错误分类样本，还纳入HDC模型正确分类但置信度较低的样本。为此引入置信度阈值，该阈值可针对每个数据集进行调整以实现最佳分类精度。所提出的训练流程在UCIHAR、CTG、ISOLET和HAND数据集上进行了测试，结果表明，在多个置信度阈值范围内，其性能相较于基线模型持续提升。扩展训练流程还促使正确分类样本的置信度值向更高区间移动，从而使分类器不仅更准确，其预测也更自信。