政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/111620
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 109952/140901 (78%)
造访人次 : 46061930      在线人数 : 1047
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 >  Item 140.119/111620


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/111620


    题名: Exploring multi-view learning for activity inferences on smartphones
    作者: Njoo, Gunarto Sindoro;Lai, Chien-Hsiang;Hsu, Kuo-Wei
    徐國偉
    贡献者: 資訊科學系
    关键词: 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
    日期: 2017-03
    上传时间: 2017-08-03 14:12:36 (UTC+8)
    摘要: 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.
    關聯: 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
    数据类型: conference
    DOI 連結: http://dx.doi.org/10.1109/TAAI.2016.7880160
    DOI: 10.1109/TAAI.2016.7880160
    显示于类别:[資訊科學系] 會議論文

    文件中的档案:

    档案 描述 大小格式浏览次数
    index.html0KbHTML2757检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈