Towards a taxonomy of interactive continual and multimodal learning for the internet of things
Towards a taxonomy of interactive continual and multimodal learning for the internet of things
复制标题
物联网交互式持续和多模式学习的分类
DOI：
10.1145/3341162.3345603
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Jan A. Persson
中科院分区：
文献类型：
--
作者：
Agnes Tegen;P. Davidsson;Jan A. Persson
随着物联网的进步，如果能够解决在多模式环境下持续学习的挑战，就会出现许多机会。在线学习中的一个常见问题是获得有标签的数据，因为这通常是昂贵的。主动学习是一种流行的有效收集标记数据的方法，但通常包括不切实际的假设。在这项工作中，我们向多模式和动态环境下的互动学习策略分类迈出了第一步。通过放松标准主动学习的假设，这些策略变得更适合现实世界的设置，并可以取得更好的表现。
With advances in Internet of Things many opportunities arise if the challenges of continual learning in a multimodal setting can be tackled. One common issue in Online Learning is to obtain labelled data, as this generally is costly. Active Learning is a popular approach to collect labelled data efficiently, but in general includes unrealistic assumptions. In this work we present a first step towards a taxonomy of Interactive Learning strategies in a multimodal and dynamic setting. By relaxing assumptions of standard Active Learning, the strategies become better suited for real-world settings and can achieve better performance.