Statistical learning with graphs

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In this talk I will review how kernel methods in machine learning allow to generalize many linear algorithms for pattern recognition or regression to non-vectorial data through implicit Hilbert space embedding, and present a few applications of these ideas when the data to be analyzed are graphs or vertices on a graph.