Graph complexity for structure and learning

author: John Shawe-Taylor, Centre for Computational Statistics and Machine Learning, University College London
published: Sept. 7, 2007,   recorded: September 2007,   views: 8967

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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.

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