DocumentCode
1933511
Title
Notice of Retraction
Semi-Supervised short text categorization based on Random Subspace
Author
YueHong Cai ; Qian Zhu ; XianYi Cheng
Author_Institution
Sch. of Foreign Language, JIANGSU Univ., Zhenjiang, China
Volume
4
fYear
2010
fDate
9-11 July 2010
Firstpage
470
Lastpage
473
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In order to solve the “labeling bottleneck” problem of short text categorization, a novel Semi-Supervised Expectation-Maximization short text categorization method based on Random Subspace (RS-EM) is used in this paper. RS-EM performs an iterative EM style training where multiple models are trained on subsets by using random subspace method. This combination of the stochastic discrimination theory and semi-supervised EM algorithm is used to compensate for the weaknesses of the standard EM algorithm to over-training. Experimental on real corpus show that the proposed method is more effectively exploit unlabeled data to enhance the learning performance, and is superior to standard semi-supervised EM algorithm in the learning efficiency and the classification generalization.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In order to solve the “labeling bottleneck” problem of short text categorization, a novel Semi-Supervised Expectation-Maximization short text categorization method based on Random Subspace (RS-EM) is used in this paper. RS-EM performs an iterative EM style training where multiple models are trained on subsets by using random subspace method. This combination of the stochastic discrimination theory and semi-supervised EM algorithm is used to compensate for the weaknesses of the standard EM algorithm to over-training. Experimental on real corpus show that the proposed method is more effectively exploit unlabeled data to enhance the learning performance, and is superior to standard semi-supervised EM algorithm in the learning efficiency and the classification generalization.
Keywords
expectation-maximisation algorithm; learning (artificial intelligence); stochastic processes; text analysis; RS-EM; random subspace; semi-supervised expectation-maximization; semi-supervised short text categorization; stochastic discrimination theory; Biological system modeling; Niobium; Pattern recognition; System-on-a-chip; EM algorithm; Random Subspace; Short Text; Text Categorization; semi-supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563807
Filename
5563807
Link To Document