원문정보
초록
영어
Human activity recognition is a main research area of context-aware computing, and is widely used in many applications, such as smart home and elderly care. Smart phone-based human activity recognition is very popular by making use of the embedded inertial sensors. However, there exists the problems of misclassification activities, and how to effectively apply the model trained by known users to new users. To solve these two problems, in this paper, we proposed a novel approach, Uncertainty Sampling based posterior Probability Extreme Learning Machine (USP-ELM), by introducing two strategies: first, we transfer the actual outputs of ELM to posterior probabilities for each instances, and then use uncertainty sampling strategy for confidence level assignment to adapt the training model and improve the classification accuracy. Experimental results show that the proposed approach is more efficient, compared with the existing ELMs.
목차
1. Introduction
2. Related Work and Preliminary
2.1. Related Work
2.2. Extreme Learning Machine
3. Proposed Approach
3.1. Posterior Probability Extreme Learning Machine
3.2. Uncertainty Sampling
4. Experimental Results
4.1. Experimental Dataset and Feature Selection
4.2. Improve the Recognition Performance with Posterior Probability ELM
4.3. Improve the Recognition Performance with Posterior Probability ELM
5. Conclusion and Future Work
References