Notice of RetractionRVM-BASED ore grade forecasting model and its application

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.