Designing similarity measures for discrete data structures, and their applications to machine learning
Project/Area Number |
20500126
|
Research Category |
Grant-in-Aid for Scientific Research (C)
|
Allocation Type | Single-year Grants |
Section | 一般 |
Research Field |
Intelligent informatics
|
Research Institution | Gakushuin University |
Principal Investigator |
KUBOYAMA Tetsuji Gakushuin University, 計算機センター, 准教授 (80302660)
|
Project Period (FY) |
2008 – 2010
|
Project Status |
Completed (Fiscal Year 2010)
|
Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2010: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2009: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2008: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
|
Keywords | 機械学習 / 類以度 / 木構造 / カーネル法 / 離散データ構造 / カーネル関数 / 編集距離 / 無順序木 / 類似度 / 距離関数 |
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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Report
(4 results)
Research Products
(46 results)