| 查看: 420 | 回复: 2 | ||
[求助]
哪位帮忙查一下这篇论文是否被EI检索了，同时麻烦给个检索情况谢谢。 已有1人参与
|
|
论文：Research and Application of Real Estate Document Image Classification Based on SVMs and KNN 第一作者：yuanchun kuang [ Last edited by cxksama on 2014-2-14 at 13:39 ] |
回复此楼» 猜你喜欢
急需调剂
已经有10人回复
-
申博/考博
已经有4人回复
-
化工学硕294分，求导师收留
已经有37人回复
-
求调剂
已经有11人回复
-
260求调剂
已经有4人回复
-
一志愿华中农业071010，320求调剂
已经有19人回复
-
304求调剂
已经有7人回复
-
求博导｜生物质基多孔碳/超级电容方向，已有相关成果，寻能源材料/碳材料方向老师
已经有3人回复
-
二苯甲酮酸类衍生物
已经有6人回复
-
接受任何调剂
已经有4人回复
» 本主题相关价值贴推荐，对您同样有帮助:
- 帮忙查一下这篇论文的检索情况
已经有3人回复
- 怎么查看自己写的会议论文是否被EI收录
已经有7人回复
- 谁能帮我查一下这个期刊EI已经检索到哪一期
已经有6人回复
- 哪位大侠帮忙查一下，这两篇文章，是否已经被SCI收录，评职称急用
已经有9人回复
triboyao
木虫 (著名写手)
- 应助: 4 (幼儿园)
- 金币: 1702.6
- 散金: 1200
- 红花: 2
- 帖子: 2947
- 在线: 164.3小时
- 虫号: 2029336
- 注册: 2012-09-25
- 性别: GG
- 专业: 运筹与管理
【答案】应助回帖
★ ★ ★ ★ ★
感谢参与，应助指数 +1
farspring: 金币+5, ★★★★★最佳答案 2014-02-14 13:27:58
感谢参与，应助指数 +1
farspring: 金币+5, ★★★★★最佳答案 2014-02-14 13:27:58
|
恭喜，检索了。 Accession number: 20140217188082 Title: Research and application of real estate document image classification based on SVMs and KNN Authors: Kuang, Yuanchun1; Yu, Jianqiao1 ; Hu, Yingchun1; Wang, Ying1 Author affiliation: 1 Institute of Computer and Information Science, Southwest University, Chongqing 400715, China Corresponding author: Yu, J. (jqyu@swu.edu.cn) Source title: Journal of Information and Computational Science Abbreviated source title: J. Inf. Comput. Sci. Volume: 10 Issue: 18 Issue date: December 10, 2013 Publication year: 2013 Pages: 6093-6100 Language: English ISSN: 15487741 Document type: Journal article (JA) Publisher: Binary Information Press, Flat F 8th Floor, Block 3, Tanner Garden, 18 Tanner Road, Hong Kong Abstract: In order to quickly and accurately classify the massive real estate documents, a novel method of automatic classification for document image is presented. Based on the paragraph and local pixel feature, it is accomplished by SVM-KNN classifiers. This method, first, extracts the paragraph and local pixel features of the preprocessed document images, then constructs the SVM-KNN multiple classifiers according to these features, finally, the feature vector set is extracted from the massive real estate document images to compare the accuracy and efficiency of SVM and KNN classifiers. The experimental results show that this method can achieve fast and accurate classification of the document images and has good application value on the automatically classification of the real estate archives. © 2013 Binary Information Press. Number of references: 11 Main heading: Image classification Controlled terms: Pattern recognition - Pixels - Support vector machines - Vector spaces Uncontrolled terms: Automatic classification - K nearest neighbor algorithm - Local pixel features - Multiple classifiers - Paragraph characteristic - Research and application - Support vector machine (SVMs) - Vector space models Classification code: 716 Telecommunication; Radar, Radio and Television - 723 Computer Software, Data Handling and Applications - 723.5 Computer Applications - 921 Mathematics DOI: 10.12733/jics20102615 Database: Compendex Compilation and indexing terms, © 2013 Elsevier Inc. |
farspring
2楼2014-02-14 13:26:27
3楼2014-02-14 13:29:26
