Abstract: A distance map and conditional probability map based on mahalanobis distance and copula function are constructed，the samples in high-dimensional space are transformed into new samples in two-dimensional space.A divided Support Vector Machine Classifier can be constructed by easily processing the new samples.It is simple and easy to implement.Experiments show that it has made better classification results.

Key words: map, two-dimensional, space, divided support vector machine classifier

摘要： 构造了基于马氏距离和Cupula函数的距离映射和条件概率映射，将高维空间中的样本转化为二维空间中的新样本，并对新样本进行简易处理，构建了可分支持向量分类机，其特点是简单，易于实现。实验表明取得了较好的分类效果。

关键词: 映射, 二维空间, 可分支持向量机