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Titlebook: Mobile Data Mining; Yuan Yao,Xing Su,Hanghang Tong Book 2018 The Author(s), under exclusive license to Springer Nature Switzerland AG 2018

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書目名稱Mobile Data Mining
編輯Yuan Yao,Xing Su,Hanghang Tong
視頻videohttp://file.papertrans.cn/636/635365/635365.mp4
叢書名稱SpringerBriefs in Computer Science
圖書封面Titlebook: Mobile Data Mining;  Yuan Yao,Xing Su,Hanghang Tong Book 2018 The Author(s), under exclusive license to Springer Nature Switzerland AG 2018
描述.This SpringerBrief presents a typical life-cycle of mobile data mining applications,?including:.data capturing and processing which determines what data to?collect, how to collect these data, and how to reduce the noise in the data?based on smartphone sensors.?feature engineering which extracts and?selects features to serve as the input of algorithms based on the collected?and processed data.?model and algorithm design.In particular, this brief concentrates?on the model and algorithm design aspect, and explains three challenging requirements?of mobile data mining applications: energy-saving, personalization, and real-time.?Energy saving is a fundamental requirement of mobile applications, due to the?limited battery capacity of smartphones. The authors ?explore the existing?practices in the methodology level (e.g. by designing hierarchical models) for saving energy. Another fundamental requirement of mobile?applications is personalization. ?Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized?treatments for mobile applications, as the behaviors may differ greatly from?one user to another in many mobile applications.
出版日期Book 2018
關(guān)鍵詞Mobile data; data mining; data capturing; energy-saving; personalization; online update; travel mode detec
版次1
doihttps://doi.org/10.1007/978-3-030-02101-6
isbn_softcover978-3-030-02100-9
isbn_ebook978-3-030-02101-6Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s), under exclusive license to Springer Nature Switzerland AG 2018
The information of publication is updating

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2191-5768 ors provide existing personalized?treatments for mobile applications, as the behaviors may differ greatly from?one user to another in many mobile applications. 978-3-030-02100-9978-3-030-02101-6Series ISSN 2191-5768 Series E-ISSN 2191-5776
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Book 2018equirement of mobile?applications is personalization. ?Most of the existing methods tend to train generic models for all users, but the authors provide existing personalized?treatments for mobile applications, as the behaviors may differ greatly from?one user to another in many mobile applications.
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2191-5768 nes what data to?collect, how to collect these data, and how to reduce the noise in the data?based on smartphone sensors.?feature engineering which extracts and?selects features to serve as the input of algorithms based on the collected?and processed data.?model and algorithm design.In particular, t
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Kenneth H. Rubin,Margaret Wilkinsonources if the user has been personally affected by conflict. Reviewing qualitative evidence in turn, the argument that quicker access, low levels of trust in formal mechanism, and hyperbolic time discounting may be the primary mechanisms through which such decisions are made, points to greater demand-side changes than supply side changes.
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