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Online Model

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Book cover Mobile Data Mining

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

Model updating is important for mobile data mining tasks. When more labeled data are available for a given target user, the mobile data mining algorithm should incorporate such data in order to provide more accurate services for the user. However, directly re-training the model with both the old and the new data would be resource consuming especially for mobile applications. A more desired way is to incrementally update the model in an online fashion. In this chapter, we introduce an online model for the mobile data mining tasks. The online model is orthogonal with the hierarchical model and personalized model. The basic idea is to adopt the stochastic sub-gradient descent method and updates the learning models with a small portion of new data.

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Authors and Affiliations

  1. Nanjing University, Nanjing, China

    Yuan Yao

  2. City University of New York, New York, USA

    Xing Su

  3. Arizona State University, Tempe, USA

    Hanghang Tong

Authors
  1. Yuan Yao
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  2. Xing Su
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  3. Hanghang Tong
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© 2018 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Yao, Y., Su, X., Tong, H. (2018). Online Model. In: Mobile Data Mining. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-030-02101-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-02101-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02100-9

  • Online ISBN: 978-3-030-02101-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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