文章基本信息

标题：Design and Analysis of Convolution Kernels for Tree-Structured Data
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作者：Hisashi Kashima ; Hiroshi Sakamoto ; Teruo Koyanagi 等
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：2006
卷号：21
期号：1
页码：113-121
DOI：10.1527/tjsai.21.113
出版社：The Japanese Society for Artificial Intelligence
摘要：We introduce a new convolution kernel for labeled ordered trees with arbitrary subgraph features, and an efficient algorithm for computing the kernel with the same time complexity as that of the parse tree kernel. The proposed kernel is extended to allow mutations of labels and structures without increasing the order of computation time. Moreover, as a limit of generalization of the tree kernels, we show a hardness result in computing kernels for unordered rooted labeled trees with arbitrary subgraph features.
关键词：kernel methods ; convolution kernels ; tree kernels ; support vector machines