文章基本信息

标题：Engineering Positive Semidefinite Kernels for Trees A Framework and a Survey
本地全文：下载
作者：Kilho Shin
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2009
卷号：24
期号：6
页码：459-468
DOI：10.1527/tjsai.24.459
出版社：The Japanese Society for Artificial Intelligence
摘要：This paper proposes two frameworks to be used in engineering tree kernels. One is to ensure that the resulting tree kernels be positive semidefinite, while the other is for efficient algorithms to compute the kernels based on the dynamic programming. The first framework provides a method to construct tree kernels using primitive kernels for simpler structures ( e.g. for labels, strings) as building blocks and a easy-to-check sufficient condition for the resultant tree kernels to be positive semidefinite. The second framework provides a set of templates of algorithms to calculate a wide range of tree kernels in O (|X|³) or O (|X|2)-time, where |X| denotes the number of vertices of trees.
关键词：machine learning ; kernel method ; convolution kernel ; tree