TY - CHAP
T1 - Online Model
AU - Yao, Yuan
AU - Su, Xing
AU - Tong, Hanghang
N1 - Publisher Copyright:
© 2018, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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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U2 - 10.1007/978-3-030-02101-6_6
DO - 10.1007/978-3-030-02101-6_6
M3 - Chapter
AN - SCOPUS:85056609384
T3 - SpringerBriefs in Computer Science
SP - 43
EP - 50
BT - SpringerBriefs in Computer Science
PB - Springer
ER -