支持向量机二次训练的简化

Jinglong Fang, Shuo Chen, Zhigeng Pan, Yigang Wang
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引用次数: 3

摘要

对于复杂的识别问题,由于这次将一些样本错误地分割成分段,导致支持向量数量多,识别速度慢。为了解决这一问题,提出了一种基于最小误估计余量思想的支持向量机简化方法。实验表明,该支持向量机不仅减少了支持向量的数量和识别时间,而且具有与传统支持向量机相同(甚至更好)的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Simplification to Support Vector Machine for the Second Training
For complicated recognition problem, the number of support vectors is large and recognition speed is low, because some sample were divided into section by error this time. To solve this problem, a method is bought to simplify the support vector machines based the minimal misestimate margin idea. Experiments show that this new support vector machine not only reduces the number of support vectors and recognition time but also has the same accuracy as (even better than) traditional support vector machine.
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