【摘要】 最小二乘支持向量机相比传统的支持向量机,丧失了解的稀疏性,影响了二次学习的效率。对原有的最小二乘支持向量机在稀疏性上进行了改进,并通过实验,对改进后的最小二乘支持向量机的分类效果进行了验证。更多还原

【Abstract】 Compared with the classical Support Vector Machines,the Least Squares Support Vector Machines lose the sparseness,which would influence the efficiency of re-learning.To conclude a sparse solution,we present an improved algorithm for Least Squares Support Vector Machines,and prove its effect by an experiment.更多还原