Graph complexity for structure and learning
author:
John Shawe-Taylor,
University of London
Description
The talk will consider ways of bounding the complexity of a graph as measured
by the number of partitions satisfying certain properties. The approach adopted uses
Vapnik Chervonenkis dimension techniques. An example of such a bound was given
by Kleinberg et al (2004) with an application to network failure detection. We describe
a new bound in the same vein that depends on the eigenvalues of the graph Laplacian.
We show an application of the result to transductive learning of a graph labelling from
examples.
Categories
Top: Computer Science: Machine Learning: Computational Learning TheoryTop: Computer Science: Machine Learning: Structured data
Top: Mathematics: Graph Theory
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