Mercer Kernel图模型学习  

Learning with Mercer Kernel graphical models

作　　者：姜凯雯[1] 张芬[1] 董洁[1] 

机构地区：[1]天津理工大学计算机与通信工程学院,天津300191

出　　处：《天津理工大学学报》2010年第2期74-76,共3页Journal of Tianjin University of Technology

摘　　要：文中提出一类学习图模型结构的算法,该算法是基于一种称为kernel generalized variance (KGV)的度量方法.此度量方法允许我们在由Mercer核产生的特征空间中处理高斯变量.进而我们能学习包含任意类型的离散和连续变量的图.文中还研究了该方法的计算性能,给出如何在线性时间内完成相关统计的计算.并用离散和连续变量进行测试.We present a class of algorithms for learning the structure of graphical models from data.The algorithms are based on a measure known as the kernel generalized variance(KGV),which essentially allows us to treat all variables on an equal footing as Gaussians in a feature space obtained from Mercer kernels.Thus we are able to learn hybrid graphs involving discrete and continuous variables of arbitrary type.We explore the computational properties of our approach,showing how to use the kernel trick to compute t...

关 键 词：Mercel核 图模型 核学习 KGV 

分 类 号：O157.5[理学—数学]