欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
由支持向量机算法得到的支持向量集合通常不是分类所必需的最小集合，冗余支持向量的存在降低了支持向量机的分类速度和实用化能力．为此，提出一种精简支持向量集合的新方法，给出了从原支持向量集合中识别和剔除冗余向量、生成新支持向量集合并确定其元素权值的算法．新方法尤其适用于样本规模大、支持向量数目多的分类问题．实验表明它能够在基本不降低支持向量机分类精度的前提下，大幅度地减少支持向量的数目，提高支持向量机的分类速度．
英文摘要：
The standard algorithms for training support vector machines generally produce solutions with a larger number of support vectors than are strictly necessary. Unnecessary support vectors have negative effects on support vector machines＇ classification speed and practical application. A new method is presented in order to reduce support vector set. Furthermore, an algorithm is proposed which recognizes and eliminates unnecessary support vectors from the original support vector set and computes the new reduced support vector set and its weights. The new method is especially suitable to the case of large-scale training set and large number of support vectors. The experimental results indicate that the new method can remarkably reduce the number of support vectors and increase the speed of classification in the condition that the correct rate does not decline.
同期刊论文项目
同项目期刊论文
期刊信息
位置：