Centroid-Distance-Based Synchronous Automatic Learning for Internet-of-Things (IoT) Applications

As the Internet of Things (IoT) expands its reach into virtually every domain, high-speed data processing and shorter response time are becoming more necessary than ever. Embedding machine learning algorithms in IoT devices is a possible effective solution. However, the IoT devices are typically dep...

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Bibliographic Details
Main Authors: Kong, Dequn, Bao, Yuanyuan, Chen, Wai
Format: Conference Proceeding
Language:English
Subjects:
Correlation
Data models
Heuristic algorithms
Machine learning
Machine learning algorithms
Predictive models
Target recognition
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Summary:As the Internet of Things (IoT) expands its reach into virtually every domain, high-speed data processing and shorter response time are becoming more necessary than ever. Embedding machine learning algorithms in IoT devices is a possible effective solution. However, the IoT devices are typically deployed in highly dynamic and uncontrolled environments, which makes machine learning algorithms with automatic learning mechanisms imperative. In this paper, we introduce a framework for synchronous automatic learning. By defining the concept of the centroid distance vector we propose synchronous automatic learning framework which automatically accomplish the reconfiguration of machine learning algorithm based on label refinement. Our evaluation shows that the automatic reconfiguration of machine learning algorithms is achievable with higher accuracy compared to the state-of-the-art methods.
ISSN:1938-1883
DOI:10.1109/ICC.2018.8422894