Abstract
Cross-validation is a technique used in many different machine learning approaches. Straightforward implementation of this technique has the disadvantage of causing computational overhead. However, it has been shown that this overhead often consists of redundant computations, which can be avoided by performing all folds of the cross-validation in parallel. In this paper we study to what extent such a parallel algorithm is also useful in ILP. We discuss two issues: a) the existence of dependencies between parts of a query that limit the obtainable efficiency improvements and b) the combination of parallel cross-validation with query-packs. Tentative solutions are proposed and evaluated experimentally.
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Struyf, J., Blockeel, H. (2001). Efficient Cross-Validation in ILP. In: Rouveirol, C., Sebag, M. (eds) Inductive Logic Programming. ILP 2001. Lecture Notes in Computer Science(), vol 2157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44797-0_19
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DOI: https://doi.org/10.1007/3-540-44797-0_19
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