摘要
为了有效提升支持向量机的泛化性能,提出两种集成算法对其进行训练.首先分析了扰动输入特征空间和扰动模型参数两种方式对于增大成员分类器之间差异性的作用;然后提出两种基于二重扰动机制的集成训练算法.其共同特点是,同时扰动输入特征空间和模型参数以产生成员分类器,并利用多数投票法对它们进行组合.实验结果表明,因为同时缩减了误差的偏差部分和方差部分,所以两种算法均能显著提升支持向量机的泛化性能.
For improving the generalization performance of support vector machine （SVM） effectively, two ensemble algorithms are proposed to train SVM. Firstly, the effectivity of two different disturbance mechanisms on augmenting the diversities among member classifiers, disturbing feature subspace and disturbing model parameters is analyzed. Then, two ensemble algorithms are proposed based on the double disturbance mechanism. The common character of them is that, member classifier is generated by disturbing feature subspace and model parameters, and the finial decision is made by the majority voting procedure. The experimental results show that both algorithms have the ability of improving the generalization performance of SVM significantly because they reduce the bias part and the variance part of the error simultaneously.
出处
《控制与决策》
EI
CSCD
北大核心
2008年第7期828-832,共5页
Control and Decision
基金
国家自然科学基金项目(69732010,60272095)
关键词
支持向量机
集成算法
二重扰动机制
成员分类器
Support vector machine
Ensemble algorithm
Double disturbance mechanism
Member classifier
