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- Title
基于贝叶斯推理的LS - SVM 矿产资源定量预测.
- Authors
韩创益; 王恩德; 夏建明; 崔顺哲
- Abstract
In the mineral resources quantitative prediction using the least squares support vector machine (LS-SVM), precision of results are influenced by the selection of its parameters. The prediction method based on the LS-SVM combining with Bayesian inference is proposed and it is also compared with weights-of-evidence (WofE) method. During the training process, the optimized parameters of LS-SVM are chosen by Bayesian inference method, which can build the optimized model for the mineral resources quantitative prediction. The results show that the proposed method not only overcomes randomness and limitation of its optimal parameter selection, but also increases the accuracy of prediction by exporting the prediction result in the form of posterior probability.
- Subjects
SUPPORT vector machines; BAYESIAN field theory; QUANTITATIVE analysts; PREDICTION models; PARAMETER estimation
- Publication
Journal of Northeastern University (Natural Science), 2017, Vol 38, Issue 11, p1633
- ISSN
1005-3026
- Publication type
Article
- DOI
10.12068/j.issn.1005-3026.2017.11.023