局部学习支持向量机  

Local learning based support vector machine

作　　者：陶剑文[1,2] 王士同[1] 

机构地区：[1]江南大学信息工程学院,江苏无锡214122 [2]浙江工商职业技术学院信息工程学院,浙江宁波315012

出　　处：《控制与决策》2012年第10期1510-1515,共6页Control and Decision

基　　金：国家自然科学基金项目(60975027;60903100);宁波市自然科学基金项目(2009A610080)

摘　　要：针对传统支持向量机不能较好地利用数据空间局部信息的问题,提出一种基于局部学习的支持向量机.通过同时最小化局部内散度和最大化局部间散度信息来寻求一个最优的分类决策函数.为了更好地反映数据的局部几何特征,该方法采用适于局部学习的测地线距离来度量数据点对间的相似性.另外,通过引入一个能同时控制间隔误差上界和支持向量下界的参数,进一步提升学习泛化能力.人造和实际数据集实验验证了所提出方法的有效性.The classic support vector machine（SVM） can not efficiently exploit the local information of data points, which is useful for pattern recognition. Therefore, a so-called local learning based support vector machine is presented to address those problems mentioned above, which makes full use of the local information such as intra-locality scatter and inter-locality scatter of the data sets to search an optimal decision function by minimizing the intra-locality scatter and simultaneously maximizing the inter-locality scatter. Meanwhile, the proposed method adopts geodesic distance metric to measure the distance between data, which can reflect the true local geometry of data space. In addition, an additional parameter/z is introduced to control both the super bound on the fraction of margin errors and the lower bound on the fraction of support vectors, thus improving the generalization capacity of the proposed method. Finally, extensive experiments show the effectiveness of the proposed method on the artificial and real world problems.

关 键 词：局部学习 流形学习 支持向量机 散度 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]