Titlebook: Schools in Transition; Linking Past, Presen Pauli Siljander,Kimmo Kontio,Eetu Pikkarainen Book 2017 SensePublishers-Rotterdam, The Netherla

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Jani Kukkola We describe these applications and how they have led to some interesting mathematical discoveries. Our main aim here is to provide a thorough overview of automated theory formation. A secondary aim is to promote mathematics as a worthy domain for ILP applications, and we provide pointers to mathema

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s, except the ones that introduce the ⊥ concept. We additionally prove that complete refinement operators for . cannot be . and suggest how this problem can be overcome by an MDL search heuristic. We also discuss the influence of the Open World Assumption (typically made in DLs) on example coverage.

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Jouni Peltonens, except the ones that introduce the ⊥ concept. We additionally prove that complete refinement operators for . cannot be . and suggest how this problem can be overcome by an MDL search heuristic. We also discuss the influence of the Open World Assumption (typically made in DLs) on example coverage.

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model. Besides, testing every possible pair of source and target domains to perform transference is costly. In this work, we focus on . by proposing a method that relies on probabilistic representations of relational databases and distributions learned by models to indicate the most suitable source

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Kimmo Kontio,Maximilian Sailerdiseases using fundus images collected from the UK Biobank dataset. The logical representation and reasoning inherent in ILP enhances the interpretability of the detection process. The results highlight the efficacy of ILP in few-shot learning scenarios, showcasing its remarkable generalisation perf

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Dieter Timmermannposed algorithm improves over the state-of-the-art discriminative weight learning algorithm for MLNs in terms of conditional likelihood. We also compare the proposed algorithm with the state-of-the-art generative structure learning algorithm for MLNs and confirm the results in [22] showing that for