Presentation 2005-02-25
Kernel Methods for Analyzing Structured Data
Hisashi KASHIMA,
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Abstract(in English) We introduce kernel-based approaches for analyzing structured data such as sequences, trees, and graphs. Especially, we introduce the idea of the convolution kernel that is a general framework for designing kernels for structured data, and give some examples of such kernels. Moreover, we introduce the structure mapping problem that is a generalized problem of the supervised classification problem, and kernel-based approaches for the problem.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Kernel Methods / Convolution Kernels / Marginalized Kernels / Graph Kernels / Structure Mapping / Hidden Markov Perceptron
Paper # NLC2004-126,PRMU2004-208
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Conference Information
Committee NLC
Conference Date 2005/2/18(1days)
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Paper Information
Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Kernel Methods for Analyzing Structured Data
Sub Title (in English)
Keyword(1) Kernel Methods
Keyword(2) Convolution Kernels
Keyword(3) Marginalized Kernels
Keyword(4) Graph Kernels
Keyword(5) Structure Mapping
Keyword(6) Hidden Markov Perceptron
1st Author's Name Hisashi KASHIMA
1st Author's Affiliation IBM Tokyo Research Laboratory()
Date 2005-02-25
Paper # NLC2004-126,PRMU2004-208
Volume (vol) vol.104
Number (no) 668
Page pp.pp.-
#Pages 6
Date of Issue