Because of using L2 norm, Principal Component Analysis (PCA) method is sensitive to outliers.
主成分分析方法由于使用了L2范数，因此对异常值较敏感。
The L2 norm soft margin algorithms in SVMs can change each linearly inseparable problem into a separable one.
支持向量机中的L2范数软边缘算法可以将线性不可分问题转化为线性可分问题。
Then based on it, we give an estimation on the difference between this local entropy solution and the solution of the quasi-one-dimensional problem in L2 norm.
然后在此基础上，我们讨论了上述熵解同拟一维流问题的解在L2意义下的相近程度。
The finite element methods of a class of singular linear and semilinear elliptic and parabolic problems are considered and the error estimates in weighted L2 norm are derived.
考虑了二维奇异线性及半线性椭圆和抛物问题的有限元方法，给出加权L2 模的误差估计。
The existence and uniqueness of the solution to these problems with the use of FEM are proved and optimal error estimates in weighted L2-norm are given.
本文讨论二维奇异非稳态问题的有限元方法，证明了弱解的存在唯一性，并给出有限元解的加权L2-模估计。
Secondly, by means of a superapproximation and interpolation postprocessing analysis technique, here we present optimal estimates and global superconvergence in the L2-norm for this method.
进而，我们利用超逼近分析技术得到了有限元解关于L2 -模的最优估计。
Furthermore, the optimal error estimates in the norm L2 are derived. Finally, Numerical experiment verifies the theoretical results.
进一步，对相应有限元解进行误差分析，得到其最优l 2模估计，数值实验验证了理论结果的正确性。
Furthermore, the optimal error estimates in the norm L2 are derived. Finally, Numerical experiment verifies the theoretical results.
进一步，对相应有限元解进行误差分析，得到其最优l 2模估计，数值实验验证了理论结果的正确性。
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