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.
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References
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)
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Frasconi, P. (2008). Learning with Kernels and Logical Representations. In: Blockeel, H., Ramon, J., Shavlik, J., Tadepalli, P. (eds) Inductive Logic Programming. ILP 2007. Lecture Notes in Computer Science(), vol 4894. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78469-2_1
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DOI: https://doi.org/10.1007/978-3-540-78469-2_1
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