1. 常州大学微电子与控制工程学院
2. 常州大学计算机与人工智能学院阿里云大数据学院
纸质出版:2024
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郇战, 刘艳, 李志新, 等. 基于平衡采样的主动半监督学习人类活动识别研究[J]. 传感器与微系统, 2024,(7):63-66+71.
郇战, 刘艳, 李志新, 等. 基于平衡采样的主动半监督学习人类活动识别研究[J]. 传感器与微系统, 2024,(7):63-66+71. DOI: 10.13873/J.1000-9787(2024)07-0063-04.
基于可穿戴传感器的人类活动识别研究逐渐受到人们的广泛关注。本文提出了一种基于平衡采样的主动半监督模型,在挑选样本进行标注时,将样本的不确定性和多样性一并考虑在内,挑选出类别平衡的不确定性样本。确保训练后的模型对每个类都有很好的识别性能,从而提升整体分类结果。同时,为了全部利用标记和未标记样本的信息,将主动学习和半监督学习相结合,利用损失项信息不断更新网络参数,提升模型在低注释下的识别性能。该模型在2个公开数据集上得到了验证,在确保获得较优分类准确率的同时,可以大大减少样本的人工标注工作。
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Department of Computing, Bournemouth University, Poole, UK,Department of Informatics and Automatics, Ecole des Mines, Douai, France,Department of Informatics and Automatics, Ecole des Mines, Douai, France,Department of Computing, Bournemouth University, Poole, UK.Online active learning for human activity recognition from sensory data streams[J].Neurocomputing,2020.
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