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  On Pairwise Kernels: An Efficient Alternative and Generalization Analysis

Kashima, H., Oyama, S., Yamanishi, Y., & Tsuda, K. (2009). On Pairwise Kernels: An Efficient Alternative and Generalization Analysis. In T. Theeramunkong, B. Kijsirikul, N. Cercone, & T.-B. Ho (Eds.), Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 (pp. 1030-1037). Berlin, Germany: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-C547-0 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-FE58-E
Genre: Conference Paper

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https://link.springer.com/content/pdf/10.1007%2F978-3-642-01307-2_110.pdf (Publisher version)
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 Creators:
Kashima, H, Author           
Oyama, S, Author
Yamanishi, Y, 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: Pairwise classification has many applications including network prediction, entity resolution, and collaborative filtering. The pairwise kernel has been proposed for those purposes by several research groups independently, and become successful in various fields. In this paper, we propose an efficient alternative which we call Cartesian kernel. While the existing pairwise kernel (which we refer to as Kronecker kernel) can be interpreted as the weighted adjacency matrix of the Kronecker product graph of two graphs, the Cartesian kernel can be interpreted as that of the Cartesian graph which is more sparse than the Kronecker product graph. Experimental results show the Cartesian kernel is much faster than the existing pairwise kernel, and at the same time, competitive with the existing pairwise kernel in predictive performance.We discuss the generalization bounds by the two pairwise kernels by using eigenvalue analysis of the kernel matrices.

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 Dates: 2009-04
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1007/978-3-642-01307-2_110
BibTex Citekey: 5655
 Degree: -

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Title: 13th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2009)
Place of Event: Bangkok, Thailand
Start-/End Date: 2009-04-27 - 2009-04-30

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Title: Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009
Source Genre: Proceedings
 Creator(s):
Theeramunkong, T, Editor
Kijsirikul, B, Editor
Cercone, N, Editor
Ho, T-B, Editor
Affiliations:
-
Publ. Info: Berlin, Germany : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1030 - 1037 Identifier: ISBN: 78-3-642-01306-5

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Title: Lecture Notes in Computer Science
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 5476 Sequence Number: - Start / End Page: - Identifier: -