作　　者： ; ; ;

机构地区： 北京科技大学计算机与通信工程学院计算机科学与技术系

出　　处： 《计算机工程》 2007年第4期165-167,共3页

摘　　要： 通过对CBR传统模型的分析与研究,针对传统CBR检索中主观确定特征权重的不足,提出了CBR检索的线性回归模型,该模型利用最小二乘法的线性回归性,更加科学、准确地确定各特征的权重,依据成熟的距离公式准确地求出范例的相似度,达到范例准确高效重用的目的。最后介绍了模型的实现方法,并且给出了详细的模型参数。 After studying the traditional CBR model, according to the deficiency that the feature weights are subjectively defined, this paper proposes a linear-regression model for CBR retrieval, which will improve the effectiveness of CBR retrieval model. The key idea is to decide the weight of each feature by the method of least square. And its property for linear regression helps to make the weights more exact. Thus in the model the similarity degree between cases is more precise than the traditional one, which facilitates the reuse of the existing cases greatly. It also gives one method to implement the model and describes the parameters.

关 键 词： 检索 回归 最小二乘法 权重

领　　域： [自动化与计算机技术] [自动化与计算机技术]