摘要
通过定义了一种基于数据最优分区间相似度算法,利用学习样本得单位相似度向量,并得各维数据的最优分区间.利用最优分区间得预测样本与学习样本的单位相似度向量,从而得预测样本的预测值.通过实例表明,算法所预测的结果相对误差可达百分位,并且本算法能应用到其它数据处理中,具有较广泛的通用性.
A algorithm of similarity on the data optimization partition is definited, unit similarity vector and data optimization partition are got through study samples. Using optimization partition, the unit similarity vector between forecast samples and study samples is computated, and prediction value of prediction sample is gained. The examples show that the Relative error of results got by the algorithm has the one-thousandth, and the algorithm can be applied on other data and has widespread versatility.
出处
《数学的实践与认识》
CSCD
北大核心
2009年第20期31-34,共4页
Mathematics in Practice and Theory
基金
国家自然科学基金(60661003)
赣财教[2008]147号省科技厅项目支持
校基金支持
关键词
最优分区间
单位相似度向量
预测
optimization partition
unit similarity vector
similarity agorithm
prediction
