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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Bacciu, D., Micheli, A., Sperduti, A. (2012). A Generative Multiset Kernel for Structured Data. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_8
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DOI: https://doi.org/10.1007/978-3-642-33269-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33268-5
Online ISBN: 978-3-642-33269-2
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