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  Learning to Find Graph Pre-Images

BakIr, G., Zien, A., & Tsuda, K. (2004). Learning to Find Graph Pre-Images. In C. Rasmussen, H. Bülthoff, B. Schölkopf, & M. Giese (Eds.), Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004 (pp. 253-261). Berlin, Germany: Springer.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-D823-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0005-5265-D
Genre: Conference Paper

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 Creators:
BakIr, G1, 2, Author              
Zien, A1, 2, Author              
Tsuda, K1, 2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The recent development of graph kernel functions has made it possible to apply well-established machine learning methods to graphs. However, to allow for analyses that yield a graph as a result, it is necessary to solve the so-called pre-image problem: to reconstruct a graph from its feature space representation induced by the kernel. Here, we suggest a practical solution to this problem.

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 Dates: 2004-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1007/978-3-540-28649-3_31
BibTex Citekey: 2639
 Degree: -

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Title: 26th Annual Symposium of the German Association for Pattern Recognition (DAGM 2004)
Place of Event: Tübingen, Germany
Start-/End Date: 2004-08-30 - 2004-09-01

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Source 1

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Title: Pattern Recognition: 26th DAGM Symposium, Tübingen, Germany, August 30 - September 1, 2004
Source Genre: Proceedings
 Creator(s):
Rasmussen, CE1, Editor            
Bülthoff, HH1, Editor            
Schölkopf, B1, Editor            
Giese, MA, Editor            
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 253 - 261 Identifier: ISBN: 978-3-540-22945-2

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 3175 Sequence Number: - Start / End Page: - Identifier: -