Then the sparse solution in frequency domain can be obtained through computing the weighted matrix equation.
然后求解这个加权矩阵方程，得到频率域的稀疏解。
Although the over all error is small, errors of some points may be very big. The error correction of the sparse solution is also discussed.
虽然稀疏解整体误差较小，但可能在一些点上的误差较大，为此提出对稀疏解的误差修正方法。
To conclude a sparse solution, we present an improved algorithm for Least Squares Support Vector Machines, and prove its effect by an experiment.
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。
To conclude a sparse solution, we present an improved algorithm for Least Squares Support Vector Machines, and prove its effect by an experiment.
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。
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