A SVM-Based Text Classification System for Knowledge Organization Method of Crop Cultivation

被引：0
|
作者：
Ji, Laiqing [1 ]
Cheng, Xinrong [1 ]
Kang, Li [1 ]
Li, Daoliang [1 ]
Li, Daiyi [1 ]
Wang, Kaiyi [2 ]
Chen, Yingyi [1 ]
机构：
[1] China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China
[2] Beijing Res Ctr Informat Technol Agr, Beijing 10097, Peoples R China
来源：
COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE V, PT I | 2012年 / 368卷
关键词：
Support Vector Machine (SVM); Text Classification; organization method; crop;
D O I：
暂无
中图分类号：
S [农业科学];
学科分类号：
09 ;
摘要：
The organization of crop cultivation practices is still far from completion, and Web Resources are not used adequately. This paper proposed a method, based on SVM, to organize the knowledge of crop cultivation practices efficiently from Web Resources. The knowledge organization method of crop cultivation was proposed with Good Agricultural Practices (GAP) in the application of the crop cultivation practices. It is that how to organize the existing crop cultivation knowledge, according to the requirements of crop cultivation practices. It mainly includes a text classification method and a search strategy on the knowledge of crop cultivation. For the text classificaiion method, it used a text classification method based on SVM Decision Tree; for the search strategy, it used a strategy, organized by Ontology and custom knowledge bases. The experiment shows that performance of the proposed text classification method and the knowledge organization method with wheat, is workable and feasible.
引用
收藏
页码：318 / +
页数：2
相关论文
共 50 条
  • [41] Optimal arrangements of hyperplanes for SVM-based multiclass classification
    Blanco, Victor
    Japon, Alberto
    Puerto, Justo
    [J]. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, 2020, 14 (01) : 175 - 199
  • [42] SVM-based automatic classification for protein structural domain
    Shao, Xiao-Han
    Tian, Ying-Jie
    Deng, Nai-Yang
    [J]. OPTIMIZATION AND SYSTEMS BIOLOGY, 2007, 7 : 341 - +
  • [43] Hardware Acceleration of SVM-Based Traffic Classification on FPGA
    Groleat, Tristan
    Arzel, Matthieu
    Vaton, Sandrine
    [J]. 2012 8TH INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING CONFERENCE (IWCMC), 2012, : 443 - 449
  • [44] Multiple model classification using SVM-based approach
    Ma, YQ
    Cherkassky, V
    [J]. PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4, 2003, : 1581 - 1586
  • [45] SVM-based fingerprint classification using orientation field
    Ji, Luping
    Yi, Zhang
    [J]. ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 2, PROCEEDINGS, 2007, : 724 - +
  • [46] An SVM-Based Classification Model for Migration Prediction of Beijing
    Zhang, Lan
    Luo, Lina
    Hu, Lei
    Sun, Maohua
    [J]. ENGINEERING LETTERS, 2020, 28 (04) : 1023 - 1030
  • [47] Prior knowledge SVM-based intrusion detection framework
    Zhang, Gang
    Yin, Jian
    Liang, Zhaohui
    Cai, YanGuang
    [J]. ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 2, PROCEEDINGS, 2007, : 489 - +
  • [48] SVM-based segmentation and classification of remotely sensed data
    Lizarazo, I.
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 2008, 29 (24) : 7277 - 7283
  • [49] SVM-based Decision Tree for Medical Knowledge Representation
    Huang, Yo-Ping
    Nashrullah, Muhammad
    [J]. 2016 INTERNATIONAL CONFERENCE ON FUZZY THEORY AND ITS APPLICATIONS (IFUZZY), 2016,
  • [50] A fuzzy SVM-based approach for analyse of Web Text Content
    Cheng, Xiaorong
    Wei, He
    Guo, Haojun
    [J]. ADVANCING SCIENCE THROUGH COMPUTATION, 2008, : 134 - 138
12345