The initial step of BV-SVM is to learn some preparatory knowledge from boundary vectors in kernel space. Then with samples that violate Karush-Kuhn-Tucker (KKT) conditions, the final SVM is acquired by incremental training.
算法首先利用核空间边界向量学习获得一些初步的知识,然后对违反KKT条件的样本通过增量训练得到最终的支持向量机。
参考来源 - 基于数据耕种与数据挖掘的系统效能评估方法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
最后以另一个大的比例减去位于距异类中心较远的对分类不起作用的样本点，以便提取具有代表性的边界向量。
Finally, the other large proportion is decided to reduce those sample points lie on the further from the different class center so that the representative boundary vectors can be extracted.
因此，优化解决方案是从轴的原点沿 (1,-1) 方向作为方向向量指向多面体边界的最远点。
Therefore, the optimal solution is the farthest point on the polyhedron boundary from the axis' origin using the direction (1,-1) as a direction vector.
通过保持数据对齐并针对16字节边界进行填充，向量操作就可以毫不费力地执行了。
By keeping the data aligned and padded to 16-byte boundaries, vector operations can be performed effortlessly.
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