英语翻译This learning algorithm maps the low dimensional dataset

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This learning algorithm maps the low dimensional datasets to the high dimensional feature space, and aims to solve a binary problem by searching an optimal hyperplane which can separate two datasets with the largest margin in the high dimensional space.
这种学习算法把低维数据集映射到高维特征空间,它的目的是通过找出一个能够在高将纬度特征空间内分成两个最大化的合集的最优化超平台来解决二进制的问题.
As shown in Fig. 2, the optimal hyper plane is defined by wx + b = 0, where x is the point lying in the hyperplane, w is the parameter for the orientation of hyperplane, and b is a scalar threshold which represents the bias from the margins
如图2（Fig2）所示,最优超平面符合wx+ b = 0的条件,等式中x是超平面上的特定点,w是超平面取向（定位）参数,b是一个标量阈值,代表间隔偏差（边缘偏差）.
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