DocumentCode
1960812
Title
Notice of Retraction
RVM-BASED ore grade forecasting model and its application
Author
Huixin Wu ; Feng Wang
Author_Institution
Dept. of Inf. Eng., North China Univ. of Water Conservancy & Electr. Power, Zhengzhou, China
Volume
2
fYear
2010
fDate
9-11 July 2010
Firstpage
449
Lastpage
452
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 establish 3D solid model in geological fields, the key question is to obtain complete ore grade attribute data. Traditional forecasting methods such as neural networks, support vector machine (SVM) are adopted frequently. However, these methods are lack of necessary probability information and can not acquire the uncertainty of forecasts. In this paper, a new forecasting model is proposed based on relevance vector machine (RVM). Compared with other methods, the mistake rate and time complexity of RVM is lower and RVM has not any restriction on the selection of kernel function. Test results show that the proposed method has superior non-linear forecasting ability, higher precision of prediction and broad application prospects.
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 establish 3D solid model in geological fields, the key question is to obtain complete ore grade attribute data. Traditional forecasting methods such as neural networks, support vector machine (SVM) are adopted frequently. However, these methods are lack of necessary probability information and can not acquire the uncertainty of forecasts. In this paper, a new forecasting model is proposed based on relevance vector machine (RVM). Compared with other methods, the mistake rate and time complexity of RVM is lower and RVM has not any restriction on the selection of kernel function. Test results show that the proposed method has superior non-linear forecasting ability, higher precision of prediction and broad application prospects.
Keywords
computational complexity; forecasting theory; geology; solid modelling; support vector machines; 3D solid model; RVM-based ore grade forecasting model; probability information; relevance vector machine; time complexity; Geology; Support vector machines; 3D modeling; RVM; information forecast; ore grade;
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.2010.5565174
Filename
5565174
Link To Document