为了克服LS-SVM解非稀疏性的缺点，同时抑制偏差较大的训练样本对海底趋势面构造的影响，提出一种基于局部样本中心距离的训练样本优化方法。

In order to solve the sparseness of LS-SVM results and restrain the influence of the sample-outliers, a new method of optimized samples by part samples center distance is presented.

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我还要用到这里描述的样本用例，通过减少远程调用的数量优化实例的性能。

I will use the sample use case described here and optimize its performance by reducing the number of remote calls.

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模糊数学方法的引用，使该模型不但能处理矛盾样本，而且有信息优化处理的功能。

Using fuzzy method we can not only treat with contradictory samples, but also make it in optimal case.

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