针对经过多次范例推理后范例库不断增大而导致占用存储空间大、检索速度慢等问题, 本文提出一种基于支持向量的范例约简算法, 其核心思想是找到范例库中的支持向量并确定出三个边界, 然后保留支持范例, 约简对求解新问题不起什么作用的冗余范例。实验结果表明, 该方法在牺牲很小的分类准确率的情况下, 大大减少了范例库的占用空间和检索复杂度。

The utility problem will occur after the case-based reasoning system runs many times, and this problem results in a decrease performance, such as a large storage space, a low retrieval rapid. To solve this problem, a cases reduction algorithm based on support vector is proposed, whose core idea is to study the distribution principle of all cases, and find support vectors and then decide 3 boundaries, at last, reduce the redundant cases which are not of use to solve new problem. Experimental tests indicate that, the method reduces cases in the case-base and decrease retrieval complexity at the expense of very small classification accuracy.