总间隔模糊超球学习机  

Total Margin Based Fuzzy Hypersphere Learning 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,无锡214122 [2]浙江工商职业技术学院信息工程学院,宁波315012

出　　处：《模式识别与人工智能》2012年第2期237-247,共11页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.60975027;60903100);宁波市自然科学基金(No.2009A610080)资助项目

摘　　要：为解决传统支持向量机易出现学习"过拟合"和丢失数据统计特征等问题,通过引入模糊隶属度和总间隔思想,提出一种基于总间隔的最大间隔最小包含模糊球形学习机(TMF-SSLM),使得一类(正类)被包含于一个最小包含超球内,而另一类(负类)与该超球间隔最大化,从而同时实现类间间隔的增大和正负两类类内体积的缩小.通过使用差异成本,解决不平衡训练样本问题.引入总间隔和模糊性惩罚,克服传统软间隔分类机的过拟合问题,显著提升球形学习机的泛化能力.采用UCI实际数据集分别对二类和一类模式分类进行实验,结果显示TMF-SSLM具有优于相关方法的稳定分类性能.There are several problems in classical support vector machines, such as overfitting problem resulted from the outlier and class imbalance learning and the loss of the statistics information of training examples. Aiming at these problems, a total margin based fuzzy hypersphere learning machine （TMF- SSLM） is proposed by constructing a minimum hypersphere in Mercer kernel-induced feature space. The main idea of TMF-SSLM is that one class of binary patterns is enclosed in the minimum hypersphere, from which another one is separated away with maximum margin. Thus both maximum between-class margin and minimum within-class volume are implemented. The proposed TMF-SSLM solves the overfitting problem resulted from outliers by employing both the fuzzification of the penalty and total margin algorithm, as well as the imbalanced problem by using different cost algorithm. Theoretical analysis justifies that TMF-SSLM obtains a lower generalization error bound. The exprimental results

关 键 词：总间隔 支持向量 模糊支持向量机 超球体 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]