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1·On the other hand, support vectors are the points that define the hyperplane.

另一方面，支持向量定义的平面的点。

2·This algorithm is fast, incremental learning, and it takes less support vectors!

本算法具有速度快、增量学习、使用的支持向量少等显著优点。

3·RASVM possesses less convergence support vectors, training time and higher precision of prediction, capacity of generalization.

RASVM具有更少的收敛的支持向量、训练时间和更高的预测精度和泛化能力。

4·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。

5·How is the VC dimension of a support-vector machine related to its number of support vectors? Is there a formula relating these two quantities?

一个支持向量机的支持向量数相关的VC维是怎样的？有一个公式，关于这两个量？

6·The algorithm introduced the forgetting factor to get the support vectors at the first training. The number of support vectors is decreased by 28%.

该算法在第一次小样本训练时引入了遗忘因子，该因子使支持向量数减少了28％。

7·The method firstly organizes support vectors in clusters in feature space, and then, it finds the pre-images of the cluster centroids in feature spa.

此方法首先在特征空间中对支持向量进行聚类，然后寻找特征空间中的聚类中心在输入空间中的原像以形成约简向量集。

8·The cost of the training of support vector machine is too much, if we use thousands of support vectors directly, the computation time would be too long.

支持向量机的训练代价太大，如果直接把成千上万个特征向量直接用作训练，运算时间难以忍受。

9·Thus the computational complexity is no longer depend on space dimension, but the number of samples, especially the number of support vectors in each sample.

计算的复杂度不再取决于空间维数，而是取决于样本数，尤其是样本中的支持向量数。

10·The fuzzy membership based on the affinity among samples for support vector machine effectively distinguishes between support vectors and outliers or noises.

将其应用于模糊支持向量机方法中，较好地将支持向量与含噪声或野值样本区分开。