A Generative Multiset Kernel for Structured Data

  • Davide Bacciu
  • Alessio Micheli
  • Alessandro Sperduti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7552)

Abstract

The paper introduces a novel approach for defining efficient generative kernels for structured-data based on the concept of multisets and Jaccard similarity. The multiset feature-space allows to enhance the adaptive kernel with syntactic information on structure matching. The proposed approach is validated using an input-driven hidden Markov model for trees as generative model, but it is enough general to be straightforwardly applicable to any probabilistic latent variable model. The experimental evaluation shows that the proposed Jaccard kernel has a superior classification performance with respect to the Fisher Kernel, while consistently reducing the computational requirements.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Davide Bacciu
    • 1
  • Alessio Micheli
    • 1
  • Alessandro Sperduti
    • 2
  1. 1.Dipartimento di InformaticaUniversità di PisaItaly
  2. 2.Dipartimento di MatematicaUniversità di PadovaItaly

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