摘要
支持向量机(SVM)是一种基于结构风险最小化原理的学习技术,也是一种新的具有很好泛化性能的回归方法,针对企业销售量预测问题,利用支持向量机构建其预测模型,对预测模型进行了仿真及对比实验,证实了模型的有效性。并介绍了SVR理论,同时对于SVR模型参数的选择问题进行了研究。
Support vector machine is a learning technique based on the structural risk minimization principle, and it is also a class of regression method with good generalization ability. Support vector machine is used to model enterprise sale amount prediction, and the theory of SVR is briefly described. A simulation example is taken to demonstrate correctness and effectiveness of the proposed approach. The selection method of the model parameters is presented.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2005年第1期33-36,共4页
Journal of System Simulation
基金
国家自然基金资助项目(60172063)教育部博士点基金项目(20010004004)
关键词
销售量
统计学习理论
支持向量回归
预测
sales volume
statistical learning theory
support vector regression
prediction
