DocumentCode :
1860316
Title :
Notice of Retraction
Coal Mine Enterprises Merger & Acquisition Efficiency Prediction Based on Support Vector Machine
Author :
Chen Xiang ; Cai Weihua
Author_Institution :
Inst. of Econ. & Manage., Hebei Univ. of Eng., Handan, China
fYear :
2010
fDate :
9-10 Jan. 2010
Firstpage :
564
Lastpage :
567
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.

Presently, China coal mine enterprises are under merger and reorganization great environment. These enterprises merger and acquisition (M&A) process efficiency predictions lack theoretical support. The paper constructed coal enterprises M&A efficiency evaluation system, built the M&A efficiency prediction model based on support vector machine (SVM). The paper investigated the data in 2004-2008 of coal enterprises M&A, applying the model made empirical analysis. The results show the method is feasible and effective.
Keywords :
coal; corporate acquisitions; financial management; mining industry; support vector machines; China coal mine enterprises; M&A efficiency evaluation system; merger and acquisition process efficiency predictions; support vector machine; Artificial neural networks; Corporate acquisitions; Data mining; Economic forecasting; Electronic mail; Engineering management; Environmental economics; Marketing and sales; Predictive models; Support vector machines; SVM; coal enterprises M&A; efficiency prediction; index system;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Knowledge Discovery and Data Mining, 2010. WKDD '10. Third International Conference on
Conference_Location :
Phuket
Print_ISBN :
978-1-4244-5397-9
Type :
conf
DOI :
10.1109/WKDD.2010.74
Filename :
5432489
Link To Document :
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