中文摘要：

相关向量机（Relevance Vector Machine,RVM）是一种新的在稀疏贝叶斯概率模型的基础上发展起来的基于统计学习理论的机器学习方法,它比支持向量机（Support Vector Machine,SVM）有更多优点,已成为数据挖掘的又一高效有力工具.本文研究了RVM在铜锍吹炼中的应用,用RVM对某冶炼厂铜锍吹炼过程参数进行预测,结果表明,RVM在处理小样本、非线性、高维数据时效果较好,并且在某些方面优于SVM.

英文摘要：

Relevance vector machine（RVM） technique as a new machine learning method is based on statistical learning theory,and it is developed on the basis of sparse Bayesian learning theory.RVM has more merits than support vector machine（SVM）,and it is proven to be a valid data mining tool.RVM and its application was discussed,which in copper-matte converting mainly.RVM was used to optimizing and forecasting the parameter of the copper-matte converting process of a factory,Experimental results show that RVM is very suitable for handing the small sample、nonlinear、high dimensional optimization problem,and some performances are better than SVM.