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Leveraging matrix computations for efficient learning on graphs

 
: Gärtner, T.; Vembu, S.

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Volltext (PDF; )

Ding, C. ; Association for Computing Machinery -ACM-, Special Interest Group on Knowledge Discovery and Data Mining -SIGKDD-:
Data Mining using Matrices and Tensors, DMMT '08 : Proceedings of a Workshop held in conjunction with The 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD 2008) Las Vegas, USA, August 24, 2008
New York: ACM, 2008
ISBN: 978-1-60558-307-5
8 S.
Workshop on Data Mining using Matrices and Tensors (DMMT) <2008, Las Vegas/Nev.>
International Conference on Knowledge Discovery and Data Mining (KDD) <14, 2008, Las Vegas/Nev.>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
While several kernel functions for graphs have been proposed in the past, their practical applications so far are rather limited. One reason for this could be the fact that graphs are typically large, but applying standard implementations of kernel methods with graph kernels is not the most efficient solution. In this paper, we describe several ways to implement efficient kernel methods for graphs using tools from matrix computations.

: http://publica.fraunhofer.de/dokumente/N-88545.html