By the learning errors of the LS-SVM model, most sample points of small errors are deleted from the original sample space, and thus the sparseness of the LS-SVM is obtained.

• 根据最小二乘支持向量机模型学习误差的大小，去除原变量空间中大部分误差较小的样本点，从而获得回归模型的“稀疏”特性，大大简化了模型复杂程度。