Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics

Research on Document Content Classification on Mathematical Regression Model

Authors
Hua Long, Baoan Li
Corresponding Author
Hua Long
Available Online October 2015.
DOI
10.2991/icmii-15.2015.120How to use a DOI?
Keywords
Document Classification; SVM (Support Vector Machine); CHI (Chi-square Statistic); Mathematical Regression Model
Abstract

To improve the document classification problem, this study proposes a classification algorithm based on mathematical regression model, making Chinese document classification get rid of the dependence on traditional dictionary method. The method of extracting high frequency keywords, establishes the appropriate matrix model, making a high-dimensional document change into a low-dimensional document, and then use mathematical regression model to give a comprehensive feature weighting function by corpus training. It explored an approach to avoid the traditional method of the problem of curse of dimensionality.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
Series
Advances in Computer Science Research
Publication Date
October 2015
ISBN
10.2991/icmii-15.2015.120
ISSN
2352-538X
DOI
10.2991/icmii-15.2015.120How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Hua Long
AU  - Baoan Li
PY  - 2015/10
DA  - 2015/10
TI  - Research on Document Content Classification on Mathematical Regression Model
BT  - Proceedings of the 3rd International Conference on Mechatronics and Industrial Informatics
PB  - Atlantis Press
SP  - 695
EP  - 698
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmii-15.2015.120
DO  - 10.2991/icmii-15.2015.120
ID  - Long2015/10
ER  -