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Domain Correction-Based Adaptive Extreme Learning Machine

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Abstract

This chapter presents a novel domain correction and adaptive extreme learning machines framework (DC-AELM) with transferring capability to solve the drift and interference problem of E-nose. The framework consists of two parts: (1) domain correction (DC) that makes the distributions of two domains close; (2) adaptive extreme learning machine (AELM) that learns a transferable classifier at decision level. This method is motivated by the idea of transfer learning, especially from the perspective of domain correction and decision making, to realize the knowledge transfer for interference suppression and drift compensation. Experiments on a background interference dataset and a public benchmark sensor drift dataset via E-nose verify the effectiveness of the proposed DC-AELM method.

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Authors and Affiliations

  1. College of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

    Lei Zhang

  2. College of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China

    Fengchun Tian

  3. School of Science and Engineering, Chinese University of Hong Kong (Shenzhen), Shenzhen, Guangdong, China

    David Zhang

Authors
  1. Lei Zhang
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  2. Fengchun Tian
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  3. David Zhang
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Zhang, L., Tian, F., Zhang, D. (2018). Domain Correction-Based Adaptive Extreme Learning Machine. In: Electronic Nose: Algorithmic Challenges. Springer, Singapore. https://doi.org/10.1007/978-981-13-2167-2_13

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  • DOI: https://doi.org/10.1007/978-981-13-2167-2_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2166-5

  • Online ISBN: 978-981-13-2167-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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