2010 Fiscal Year Final Research Report
Designing similarity measures for discrete data structures, and their applications to machine learning
| Project/Area Number |
20500126
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Gakushuin University |
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Principal Investigator |
KUBOYAMA Tetsuji Gakushuin University, 計算機センター, 准教授 (80302660)
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| Project Period (FY) |
2008 – 2010
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| Keywords | 機械学習 / 類以度 / 木構造 / カーネル法 |
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| Research Abstract |
The similarity measures called kernel functions for discrete data structures such as strings, trees, and graphs allow for statistical analysis and machine learning for non-numerical variables. In this study, a novel and general framework for designing kernel functions for discrete data structures has been developed. The framework is a generalization of Haussler's convolution kernel by counting all possible structural correspondences between two structures. Moreover, a diversity of similarity measures for trees have been developed based on counting their common substructures.
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