基于最小包含球的大数据集域自适应快速算法  

A Fast Learning Algorithm Based on Minimum Enclosing Ball for Large Domain Adaptation

作　　者：许敏[1,2] 王士同[1] 顾鑫[1,3] 俞林[2] 

机构地区：[1]江南大学数字媒体学院,无锡214122 [2]无锡职业技术学院物联网技术学院,无锡214121 [3]无锡北方湖光光电有限公司,无锡214035

出　　处：《模式识别与人工智能》2013年第2期159-168,共10页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.60903100;60975027;61170122);江苏省研究生创新工程项目(No.CXZZ12-0759)资助

摘　　要：相同应用领域,不同时间、地点或设备检测到的数据域不一定完整.文中针对如何进行数据域间知识传递问题,提出相同领域的概率分布差异可用两域最小包含球中心点表示且其上限与半径无关的定理.基于上述定理,在原有支持向量域描述算法基础上,提出一种数据域中心校正的领域自适应算法,并利用人造数据集和KDD CUP 99入侵检测数据集验证该算法.实验表明,这种领域自适应算法具有较好的性能.The data fields detected from different times, places or devices are not always complete even if they come from the same data resource. To solve the problem of effectively transferring the knowledge between the two fields, the theorem is proposed that the difference between two probability distributions from two domains can be expressed by the center of each domain＇s minimum enclosing ball and its up limit has nothing to do with the radius. Based on the theorem, a fast center calibration domain adaptive algorithm, center calibration-core sets support vector data description （CC-CSVDD）, is proposed for large domain adaptation by modifying the original support vector domain description （SVDD） algorithm. The validity of the proposed algorithm is experimentally verified on the artificial datasets and the real KDD CUP-99 datasets. Experimental results show that the proposed algorithm has good performance

关 键 词：领域自适应 支持向量域描述(SVDD) 最小包含球(MEB) 核心集 大数据集 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]