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    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/111620
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/111620

    Title: Exploring multi-view learning for activity inferences on smartphones
    Authors: Njoo, Gunarto Sindoro;Lai, Chien-Hsiang;Hsu, Kuo-Wei
    Contributors: 資訊科學系
    Keywords: Artificial intelligence;Classification (of information);Deep neural networks;Digital storage;Energy utilization;Activity inference;Built-in-hardware;Classification methods;Inferring activities;Location information;Multi-view learning;Spatial and temporal patterns;Storage efficiency;Smartphones
    Date: 2017-03
    Issue Date: 2017-08-03 14:12:36 (UTC+8)
    Abstract: Inferring activities on smartphones is a challenging task. Prior works have elaborated on using sensory data from built-in hardware sensors in smartphones or taking advantage of location information to understand human activities. In this paper, we explore two types of data on smartphones to conduct activity inference: 1) Spatial-Temporal: reflecting daily routines from the combination of spatial and temporal patterns, 2) Application: perceiving specialized apps that assist the user's activities. We employ multi-view learning model to accommodate both types of data and use weighted linear kernel model to aggregate the views. Note that since resources of smartphones are limited, activity inference on smartphones should consider the constraints of resources, such as the storage, energy consumption, and computation power. Finally, we compare our proposed method with several classification methods on a real dataset to evaluate the effectiveness and performance of our method. The experimental results show that our approach outperforms other methods regarding the balance between accuracy, running time, and storage efficiency. © 2016 IEEE.
    Relation: TAAI 2016 - 2016 Conference on Technologies and Applications of Artificial Intelligence, Proceedings, , 212-219
    2016 Conference on Technologies and Applications of Artificial Intelligence, TAAI 2016; National Tsing Hua UniversityHsinchu; Taiwan; 25 November 2016 到 27 November 2016; 類別編號CFP1624L-ART; 代碼 126910
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1109/TAAI.2016.7880160
    DOI: 10.1109/TAAI.2016.7880160
    Appears in Collections:[資訊科學系] 會議論文

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