著者

Tetsuji Kuboyama Kouichi Hirata Hisashi Kashima Kiyoko F.Aoki-Kinoshita Hiroshi Yasuda

出版者

The Japanese Society for Artificial Intelligence

雑誌

Transactions of the Japanese Society for Artificial Intelligence (ISSN:13460714)

巻号頁・発行日

vol.22, no.2, pp.140-147, 2007 (Released:2007-01-25)

参考文献数

17

被引用文献数

5 11 27

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Learning from tree-structured data has received increasing interest with the rapid growth of tree-encodable data in the World Wide Web, in biology, and in other areas. Our kernel function measures the similarity between two trees by counting the number of shared sub-patterns called tree q-grams, and runs, in effect, in linear time with respect to the number of tree nodes. We apply our kernel function with a support vector machine (SVM) to classify biological data, the glycans of several blood components. The experimental results show that our kernel function performs as well as one exclusively tailored to glycan properties.