Evaluation of action prediction method using inductive learning with N-gram

被引:0
|
作者
Xu, JA [1 ]
Itoh, T [1 ]
Araki, K [1 ]
Tochinai, K [1 ]
机构
[1] Hokkaido Univ, Grad Sch Engn, Kita Ku, Sapporo, Hokkaido 0608628, Japan
关键词
inductive learning; N-gram; action prediction; dynamic adaptation;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Being society aging, an intelligent room is needed for the aged or handicapped. The important ingredient of such a system is how to predict the next action. In this paper we describe how to solve the problem of predicting inhabitant action in an intelligent room that we called learning room. We have proposed a method to predict user action using Inductive Learning (IL) with N-gram. The system based on our proposed method is able to acquire the inimanent causality rules automatically from data pairs bv means of IL. Since our system unified IL and N-gram, it demonstrates good accuracy for the simulated data. The system showed high dynamic adaptive capability.
引用
收藏
页码:1605 / 1609
页数:5
相关论文
共 50 条
  • [41] Software Fault Localization Using N-gram Analysis
    Nessa, Syeda
    Abedin, Muhammad
    Wong, W. Eric
    Khan, Latifur
    Qi, Yu
    [J]. WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, PROCEEDINGS, 2008, 5258 : 548 - 559
  • [42] Multilingual Text Categorization Using Character N-gram
    Suzuki, Makoto
    Yamagishi, Naohide
    Tsai, Yi-Ching
    Hirasawa, Shigeichi
    [J]. 2008 IEEE CONFERENCE ON SOFT COMPUTING IN INDUSTRIAL APPLICATIONS SMCIA/08, 2009, : 49 - +
  • [43] Chinese Text Categorization Using the Character N-gram
    Suzuki, Makoto
    Yamagishi, Naohide
    Tsai, Yi-Ching
    [J]. 2012 INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY AND ITS APPLICATIONS (ISITA 2012), 2012, : 722 - 726
  • [44] Improved Text Generation Using N-gram Statistics
    de Novais, Eder Miranda
    Tadeu, Thiago Dias
    Paraboni, Ivandre
    [J]. ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2010, 2010, 6433 : 316 - 325
  • [45] Automatic Evaluation of Translation Quality Using Expanded N-gram Co-occurrence
    Qin, Ying
    Wen, Qiufang
    Wang, Jinquan
    [J]. IEEE NLP-KE 2009: PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING, 2009, : 550 - +
  • [46] Using n-gram analysis to cluster heartbeat signals
    Huang, Yu-Chen
    Lin, Hanjun
    Hsu, Yeh-Liang
    Lin, Jun-Lin
    [J]. BMC MEDICAL INFORMATICS AND DECISION MAKING, 2012, 12
  • [47] HTTP attack detection using n-gram analysis
    Oza, Aditya
    Ross, Kevin
    Low, Richard M.
    Stamp, Mark
    [J]. COMPUTERS & SECURITY, 2014, 45 : 242 - 254
  • [48] Intelligent Assessment Using Variable N-gram Technique
    Kar, Sadhu Prasad
    Chatterjee, Rajeev
    Mandal, Jyotsna Kumar
    [J]. IMPACT OF THE 4TH INDUSTRIAL REVOLUTION ON ENGINEERING EDUCATION, ICL2019, VOL 2, 2020, 1135 : 30 - 37
  • [49] Evaluation of N-gram term conflation approach for arabic texts
    Abu-Salem, H
    [J]. PROCEEDINGS OF 2003 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING, VOLS I AND II, 2003, : 2561 - 2567
  • [50] An Evaluation of Character Level N-gram Termsets in Text Categorization
    Coban, Onder
    Ozel, Selma Ayse
    [J]. 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP), 2018,