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Learning with Kernels and Logical Representations

  • Paolo Frasconi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4894)

Abstract

Choosing an appropriate kernel function is a fundamental step for the application of many popular statistical learning algorithms. Kernels are actually the natural entry point for inserting prior knowledge into the learning process. Inductive logic programming (ILP), on the other hand, offers a powerful and flexible framework for describing existing background knowledge and extracting additional knowledge from the data. It therefore seems natural to explore the synergy between these two important paradigms of machine learning. In this extended abstract (see [1] for a longer version), I briefly review some of our recent work about statistical learning with kernel machines in the ILP setting.

Keywords

Kernel Function Logic Program Logical Representation Inductive Logic Programming Execution Trace 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Frasconi, P., Passerini, A.: Learning with kernels and logical representations. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.) Application of Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911. Springer, Heidelberg (2008)Google Scholar
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    De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S.: Application of Probabilistic Inductive Logic Programming. LNCS (LNAI), vol. 4911. Springer, Heidelberg (2008)Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Paolo Frasconi
    • 1
  1. 1.Dipartimento di Sistemi e InformaticaUniversità degli Studi di FirenzeItaly

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