【摘要】 通过对欧氏距离度量的分析,提出了自适应距离度量。首先利用训练样本建立自适应距离度量模型,该模型保证了训练样本到相同模式类的距离最近,到不同模式类的距离最远,根据该模型建立目标函数,求解目标函数,得到最优权重。基于最小距离分类器和K近邻分类器,采用UCI标准数据库中部分数据,对提出的自适应距离度量和欧氏距离度量进行了实验比较,实验结果表明自适应距离度量更有效。更多还原

【Abstract】 After the Euclidian distance metric is analyzed,a new training method based on adaptive distance metric is proposed,which adopts a model about adaptive distance metric,the model assures that the distance between training sample and the same pattern classi-fication is nearest,and the distance between training sample and other pattern classification is far,then a optimal weight is obtained through solving objective function.By adding weight define for distance in the classification phase,the classifier improved its classifi-cation accuracy.Experiment is tested on UCI standard database,the results show that the proposed minimum distance classifier and K-nearest neighbor classifier based on adaptive distance metric is effective,and it is superior to Euclidian distance metric in classification performance.更多还原