| Summary: | 碩士 === 國立臺灣科技大學 === 資訊工程系 === 103 === With the rapidly development of Internet of Things (IoT) and big data research, the background technology has been widely discussed. In building IoT smart environments, human activity recognition plays an important role, leading to the challenge of annotating and dealing with large-scale data. In this thesis, we use active learning as our basic framework and apply it to human activity recognition. According to different variety scenarios, several query strategies are designed. Besides, we consider that in the reality, data comes in an ordered fashion. The querying strategies are designed for streaming data to decide whether to include the newly coming instance or not.
Due to the difficulty of labeling and training large-scale data, we propose limited-time active learning. The algorithm tends to query labels and update the model at the very beginning. As time goes by, it queries much fewer labels. Therefore, we can achieve the goal to limit the number of changes done to the model. At the same time, we also solve the difficulty of expensive labeling.
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