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。
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。
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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