摘要
将一种改进的邻域算法应用于不均衡样本集中,由于改进的邻域算法未考虑不均衡样本集的问题从而导致后续的支持向量机训练耗费和泛化性能受影响,把后验概率的思想加入改进的邻域算法中,并由实验数据说明了该方法对不均衡样本集的有效性。
An improved neighborhood algorithm is applied in unbalance data set.Because of not considering unbalance date set, the generalization of improved neighborhood algorithm is degrade.In order to solve this problem,the idea of posterior probability is used in the new neighborhood algorithm and the result of pattern classification shows the effectiveness of the method.
出处
《计算机工程与应用》
CSCD
北大核心
2010年第10期36-37,40,共3页
Computer Engineering and Applications
关键词
支持向量机(SVM)
邻域算法
后验概率
VC维
Support Vector Machine（SVM）
neighborhood algorithm
posterior probability
VC dimension
