【摘要】 针对训练样本集的分类特征优化选择问题,改进了样本可分度标准:Kullback-Leiber距离,并进行了有效性验证.在此基础上定义了欧氏距离分布熵(Distribution Entropy of Euclidian Distance DEED)这一空间分布信息度量参数,同时给出了它的计算方法.提出了“类间互欧氏距离分布熵”(between-class DEED)与“类内自欧氏距离分布熵”(within-classDEED)的分析方法.进一步将其用于样本可分性分析,验证了两者比值愈大,特征样本集可分度愈好这一结论.更多还原

【Abstract】 An improved Kullback-Leiber distance is presented as a separable criterion for optimizing feature selection problems in pattern classification.A nonlinear parameter,Distributive Entropy of Euclidian Distance(DEED),is introduced and based on which,the ratio of between-class DEED to within-class DEED(JRd) is defined as a criterion for the feature selection.DEED is a nonlinear measure for learning feature space,which gives the congregation and information measure of learning samples space.According to the result of Gaussian data experiments,it is concluded that the larger JRd be,the better separability of learning samples would be.更多还原