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A Quantum Classifier Based Active Machine Learning for Intelligent Interactive Service

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Simulation Tools and Techniques (SIMUtools 2020)

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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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Acknowledgements

This work is partly supported by Jiangsu technology project of Housing and Urban-Rural Development (No. 2018ZD265) and Jiangsu major natural science research project of College and University (No. 19KJA470002).

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Authors and Affiliations

  1. Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou University of Technology, Xuzhou, 221000, China

    Jiamin Cheng, Lei Chen & Ping Cui

Authors
  1. Jiamin Cheng
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  2. Lei Chen
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  3. Ping Cui
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Corresponding author

Correspondence to Lei Chen .

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Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

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Cheng, J., Chen, L., Cui, P. (2021). A Quantum Classifier Based Active Machine Learning for Intelligent Interactive Service. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_26

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  • DOI: https://doi.org/10.1007/978-3-030-72795-6_26

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-72795-6

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