EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 232 (2018) MATEC Web Conf., 232 (2018) 01039 References
Open Access
Issue
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 01039
Number of page(s) 5
Section Network Security System, Neural Network and Data Information
DOI https://doi.org/10.1051/matecconf/201823201039
Published online 19 November 2018
  1. Song Fuxing. Design and Implementation of Spark-Based Super Large Text Classification method [D]. Beijing Jiaotong University, 2017. [Google Scholar]
  2. Wang Xiaolin, Lu Luoyong, Shao Weipeng. Research on Similarity Algorithm of New Words Based on Information Entropy [J]. Computer Technology and Development, 2015(9): 119-122. [Google Scholar]
  3. Lu L R, Yun Fa H U. A Density-Based Method for Reducing the Amount of Training Data in kNN Text Classification[J]. Journal of Computer Research & Development, 2004, 41(4): 539-545. [Google Scholar]
  4. Yu Pingping, Ni Jiancheng, Yao Binxiu, et al. Efficient KNN Chinese Text Classification Algorithm Based on Spark Framework [J]. Journal of Computer Applications, 2016, 36 (12): 3292-3297. [Google Scholar]
  5. Cheng Kefei, Zhang Cong. Naive Bayes Classifier Based on Feature Weighting[J]. Computer Simulation, 2006, 23 (10): 92-94. [Google Scholar]
  6. Li Ronglu, Wang Jianhui, Chen Xiaoyun, et al. Chinese Text Classification Using Maximum Entropy Model[J]. Journal of Computer Research and Development, 2005, 42 (1): 94-101. [CrossRef] [Google Scholar]
  7. Yuan R, Li Z, Guan X, et al. An SVM-based machine learning method for accurate internet traffic classification[J]. Information Systems Frontiers, 2010, 12(2): 149-156. [CrossRef] [Google Scholar]
  8. Ding Zhenguo, Li Jing, Zhang Zhuo. An Improved Text Classification Algorithm Based on Neural Network[J]. Application Research of Computers, 2008, 25(6): 1639-1641. [Google Scholar]
  9. Wang Yu, Wang Zhengou. Text Classification Rule Extraction Based on Fuzzy Decision Tree[J]. Journal of Computer Applications, 2005, 25(7): 1634-1637. [Google Scholar]
  10. Liu Wenjun, Zheng Guoyi, Zhang Xiaoqiong. Sample Classification Algorithm Based on Rough Set and Statistical Learning Theory[J]. Fuzzy Systems and Mathematics, 2015, 29(1): 183-190. [Google Scholar]
  11. Li Tao, Liu Bin. Research on Efficient Web Text Classification System under Spark Platform[J]. Computer Applications and Software, 2016, 33 (11): 33-36. [Google Scholar]
  12. Luo Yuanshuai. Research on parallel text classification algorithm based on random forest and Spark[D]. Southwest Jiaotong University, 2016. [Google Scholar]
  13. Sahami M, Dumais S, Heckerman D, et al. A Bayesian Approach to Filtering Junk E-Mail[J]. Papers from the Workshop Aaai, 1998. [Google Scholar]
  14. Su Sui, Lin Hongfei, Ye Zheng. Spam Filtering Based on Character Language Model [C]// National Conference on Information Retrieval and Content Security. 2008:41-47. [Google Scholar]
  15. Pan Wenfeng. Content-Based Spam Filtering Research [D]. Institute of Computing Technology, Chinese Academy of Sciences, 2004. [Google Scholar]
  16. Wang Xiujun, Shen Hong. An Efficient Text Classification Algorithm Based on Incremental Learning Vector Quantization[J]. Chinese Journal of Computers, 2007, 30(8): 1277-1285. [Google Scholar]
  17. Bratko A, Cormack G V, Lynam T R. Spam Filtering Using Statistical Data Compression Models[J]. Journal of Machine Learning Research, 2006, 7(4): 2673-2698. [Google Scholar]
  18. Giannakopoulos G, Palpanas T. Revisiting the effect of history on learning performance: the problem of the demanding lord[J]. Knowledge & Information Systems, 2013, 36(3): 653-691. [CrossRef] [Google Scholar]
  19. Wang J, Zhao Z Q, Hu X, et al. Online group feature selection[C]// International Joint Conference on Artificial Intelligence. 2014:1757-1763. [Google Scholar]
  20. Dietterich T G, Domingos P, Getoor L, et al. aStructured machine learning: the next ten years[J]. Machine Learning, 2008, 73(1):3. [CrossRef] [Google Scholar]
  21. Valentini G, Masulli F. Ensembles of Learning Machines.[C]// Italian Workshop on Neural Nets-Revised Papers. Springer-Verlag, 2002:3-22. [Google Scholar]
  22. Karpathy A, Toderici G, Shetty S, et al. Large-Scale Video Classification with Convolutional Neural Networks[C]// IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2014: 1725-1732. [Google Scholar]
  23. Gharehchopogh F S, Khaze S R, Maleki I. aA New Approach in Bloggers Classification with Hybrid of K-Nearest Neighbor and Artificial Neural Network Algorithms[J]. Indian Journal of Science & Technology, 2015, 8(3):237. [CrossRef] [Google Scholar]
  24. Kim Y. Convolutional Neural Networks for Sentence Classification[J]. Eprint Arxiv, 2014. [Google Scholar]
  25. Collier N. Convolutional Neural Network Architectures for Matching Natural Language Senences[J]. 2015, 3:2042-2050. [Google Scholar]
  26. S Lai,L Xu, K Liu. Recurrent Convolutional Neural Networks for Text Classification [Google Scholar]
  27. Alex Rudnicky. Can Artificial Neural Networks Learn Language Models?[C]// The Proceedings of the. 2000:202-205. [Google Scholar]
  28. Bengio Y, Vincent P, Janvin C. A neural probabilistic language model[J]. Journal of Machine Learning Research, 2006, 3(6): 1137-1155. [Google Scholar]
  29. Song Fuxing. Design and Implementation of Spark-Based Super Large Text Classification Method [D]. Beijing Jiaotong University, 2017. [Google Scholar]
  30. Zhou Qinqiang, Sun Bingda, Wang Yi. Research on Text Preprocessing Method of Text Automatic Classification System[J]. Application Research of Computers, 2005, 22(2): 85-86. [Google Scholar]
  31. Liu Keqiang. Analysis and Use of ICTCLAS of 2009 Shared Edition[J]. Science and Education Journal, 2009(22):271-271. [Google Scholar]
  32. Huang Yibiao. Comparative Study of Chinese Word Segmenter for Implementing Lucene Interface [J]. Science & Technology Information, 2012(12):246-247. [Google Scholar]
  33. He Jinfeng. Research on Text Preprocessing Based on Chinese Information Retrieval[D]. University of Electronic Science and Technology of China, 2008. [Google Scholar]
  34. Sun Dianzhe, Wei Haiping, Chen Yan, Realization and Evaluation of Chinese Word Segmentation of Dismemberment of Ox by Paodin in Nutch[J]. Computer and Modernization, 2010(6):187-190. [Google Scholar]
  35. Ye Na. Research on Text Preprocessing and Rule Automatic Learning Technology for Information Extraction[D]. Northeastern University, 2005. [Google Scholar]
  36. Zhang Ning. Semantic-Based Chinese Text Preprocessing Research [D]. Xidian University, 2011. [Google Scholar]
  37. Li Ying. Research on Text Preprocessing Method Based on Part of Speech Selection[J]. Intelligence Science, 2009, 27 (5): 717-719. [Google Scholar]
  38. Luo Yuanshuai. Research on Parallel Text Classification Algorithm Based on Random Forest and Spark[D]. Southwest Jiaotong University, 2016. [Google Scholar]
  39. Feng Shuxiao, Xu Xin, Yang Chunmei. New Progress of Chinese Word Segmentation Technology in China[J]. Journal of Intelligence, 2002, 21 (11): 29-30. [Google Scholar]
  40. Long Shuquan, Zhao Zhengwen, Tang Hua. Overview of Chinese Word Segmentation Algorithm[J]. Computer Knowledge and Technology, 2009, 5(4): 2605-2607. [Google Scholar]
  41. Zhai Fengwen, He Fengling, Zuo Wanli. Chinese word segmentation method combining dictionary and statistics[J]. Journal of Chinese Computer Systems, 2006, 27(9): 1766-1771. [Google Scholar]
  42. Liu Yang, Guo Qiaojin, Zhou Pengfei, et al. A Spark-based rapid extraction method for massive text keyword: CN 106202556 A [P]. 2016. [Google Scholar]
  43. Xu Yang, Liu Gongshen, Meng Kui. Text Vectorization Algorithm Based on the Relationship between Words in Sentences [J]. Information Security and Communication Privacy, 2014 (4): 84-88. [Google Scholar]
  44. Xiong Dakang. Research and Implementation of Chinese Short Text Classification Technology [D]. Anhui University, 2014. [Google Scholar]
  45. Zheng Lin, Xu Dehua. Text Classification Research Based on Improved TFIDF Algorithm[J]. Computer and Modernization, 2014 (9): 6-9. [Google Scholar]
  46. Zhang Yufang, Peng Shiming, Lu Jia. Improvement and Application Based on Text Classification TFIDF Method[J]. Computer Engineering, 2006, 32 (19): 76-78. [Google Scholar]
  47. Zhang Jinrui. Research and Application of Text Classification Based on LDA[D]. Zhengzhou University, 2016. [Google Scholar]
  48. Guo Lantian, Li Yang, Mu Dejun, et al. A Topic Discovery Method Based on LDA Theme Model[J]. Journal of Northwest Polytechnic University, 2016, 34 (4): 698-702. [Google Scholar]
  49. Wei Qiangshen. Domain Keyword Extraction: Combining LDA with Word2Vec[D]. Guizhou Normal University, 2016. [Google Scholar]
  50. Feng Guichuan. Text Modeling Based on Word2vec and Classification Research[D]. Shenzhen University, 2016. [Google Scholar]
  51. Sui Hao. Research on the Recognition and Tendency Judgment of Weibo Emotional New Words Based on Word2Vec[D]. Guangxi University, 2016. [Google Scholar]
  52. Yan Jiaming. Research and Application of Text Classification Based on Cloud Computing[D]. Zhejiang Sci-Tech University, 2016. [Google Scholar]
  53. Ren Yitian. Research on Massive Text Classification Parallelization Technology Based on Support Vector Machine[D]. Beijing Institute of Technology, 2016. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.