This algorithm is a combination of kernel trick with the covering algorithm, and is used to extract the support vectors in feature space.

该算法将核技巧与覆盖算法相结合，并在特征空间中抽取支持向量。

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It has many advantages, such as using kernel function to avoid local minimal point, sparse nature of solutions, limit used to control capacity or the number of support vectors, etc.

它拥有众多的优良特性，如利用核技术避免了解的局部最小、具有解的稀疏性、通过界限的作用达到容量控制或支持向量数目的控制等等。

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It has many advantages, such as using kernel function to avoid local minimal point, sparse nature of solutions, limit used to control capacity or the number of support vectors, etc.

它拥有众多的优良特性，如利用核技术避免了解的局部最小、具有解的稀疏性、通过界限的作用达到容量控制或支持向量数目的控制等等。

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