A Spectrum Tree Kernel
A Spectrum Tree Kernel
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DOI：
10.1527/tjsai.22.140
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发表时间：
2007
影响因子：
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通讯作者：
Tetsuji Kuboyama;Kouichi Hirata;H. Kashima;Kiyoko F. Aoki-Kinoshita;H. Yasuda
中科院分区：
文献类型：
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作者：
Tetsuji Kuboyama;Kouichi Hirata;H. Kashima;Kiyoko F. Aoki-Kinoshita;H. Yasuda
随着万维网、生物学和其他领域中树形编码数据的快速增长，从树形结构数据中学习已经受到越来越多的关注。我们的核函数通过计算共享子模式（称为树q-grams）的数量来度量两棵树之间的相似性，实际上，它是在相对于树节点数量的线性时间内运行。我们应用核函数和支持向量机（SVM）对生物数据进行分类，包括几种血液成分的聚糖。实验结果表明，我们的核函数表现得和专门针对聚糖性质的核函数一样好。
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.