Research Article
A Quantum Classifier Based Active Machine Learning for Intelligent Interactive Service
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@INPROCEEDINGS{10.1007/978-3-030-72795-6_26, author={Jiamin Cheng and Lei Chen and Ping Cui}, title={A Quantum Classifier Based Active Machine Learning for Intelligent Interactive Service}, proceedings={Simulation Tools and Techniques. 12th EAI International Conference, SIMUtools 2020, Guiyang, China, August 28-29, 2020, Proceedings, Part II}, proceedings_a={SIMUTOOLS PART 2}, year={2021}, month={4}, keywords={Response time Quantum classifier Active learning Machine learning Human--computer interaction}, doi={10.1007/978-3-030-72795-6_26} }- Jiamin Cheng
Lei Chen
Ping Cui
Year: 2021
A Quantum Classifier Based Active Machine Learning for Intelligent Interactive Service
SIMUTOOLS PART 2
Springer
DOI: 10.1007/978-3-030-72795-6_26
Abstract
The response time of interactive services depends not only on network latency, but also on computer time. Active learning algorithms are the most important methods. One problem is that these algorithms with uncertain sampling strategies propose an active learning sampling strategy on the basis of sample error correction to ensure that the efficiency and accuracy of interactive information calling are improved, and they have high computational complexity. However, due to computational complexity, this method is only suitable for smaller data sets. This article discusses the use of quantum clusters to accelerate calculations
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