Active transfer learning for activity recognition
Active transfer learning for activity recognition
复制标题
用于活动识别的主动迁移学习
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Peter A. Flach
中科院分区：
文献类型：
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作者：
Tom Diethe;N. Twomey;Peter A. Flach
.我们研究了来自加速度计的活动识别，这为机器学习提供了至少两个主要挑战。首先，部署上下文可能与学习上下文不同。其次，准确标记训练数据既耗时又容易出错。这就需要结合主动学习和迁移学习。我们推导出一个分层贝叶斯模型，这是一个自然适合这些问题，并提供合成和公开可用的数据集的经验验证。结果表明，通过结合主动学习和迁移学习，我们可以在目标域上使用更少的标签实现更快的学习，而不是单独使用。
. We examine activity recognition from accelerometers, which provides at least two major challenges for machine learning. Firstly, the deployment context is likely to differ from the learning context. Secondly, accurate labelling of training data is time-consuming and error-prone. This calls for a combination of active and transfer learning. We derive a hierarchical Bayesian model that is a natural fit to such problems, and provide empirical validation on synthetic and publicly available datasets. The re-sults show that by combining active and transfer learning, we can achieve faster learning with fewer labels on a target domain than by either alone.