Titlebook: Inductive Logic Programming; 27th International C Nicolas Lachiche,Christel Vrain Conference proceedings 2018 Springer International Publis

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Positive and Unlabeled Relational Classification Through Label Frequency Estimation,lore if using the label frequency would also be useful when working with relational data and (2) to propose a method for estimating the label frequency from relational positive and unlabeled data. Our experiments confirm the usefulness of knowing the label frequency and of our estimate.

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Stacked Structure Learning for Lifted Relational Neural Networks,ll possible Horn clauses, considering the predicates that occur in the training examples as well as invented soft concepts entailed by the best weighted rules found so far. In the experiments, we demonstrate the ability to automatically induce useful hierarchical soft concepts leading to deep LRNNs with a competitive predictive power.

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0302-9743 ng, ILP 2017, held in Orléans, France, in September 2017.. The 12 full papers presented were carefully reviewed and selected from numerous submissions.. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation langu

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On Applying Probabilistic Logic Programming to Breast Cancer Data,and rules reflecting domain knowledge were introduced. A PILP predictive model was built automatically from this data and experiments show that it can not only match the predictions of a team of experts in the area, but also consistently reduce the error rate of malignancy prediction, when compared to other non-relational techniques.

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Parallel Online Learning of Event Definitions,es, in a single pass over a data stream. In this work we present a version of . that allows for parallel, online learning. We evaluate our approach on a benchmark activity recognition dataset and show that we can reduce training times, while achieving super-linear speed-ups on some occasions.

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