基于贝叶斯推理的LS-SVM矿产资源定量预测

针对矿产资源定量预测过程中最小二乘支持向量机(LS-SVM)的参数选择具有主观性和随意性,提出了一种与贝叶斯推理相结合的LS-SVM资源定量预测方法,并将其与证据权法(Wof E)进行了对比.在训练过程中采用贝叶斯推理方法对LS-SVM的参数选择进行优化,进而构建矿产资源定量预测优化模型.研究表明,该方法不但克服了参数选择的局限性,而且以后验概率形式输出预测结果,从而可提高预测精度.

Mineral Resource Quantitative Prediction Based on LS-SVM Combining with Bayesian Inference

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.