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An Empirical Investigation into Deep and Shallow Rule Learning

2021

Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that directly relate the input features to the target concept. In the simplest case, concept learning, this is a disjunctive normal form (DNF) description of the positive class. While it is clear that this is sufficient from a logical point of view because every logical expression can be reduced to an equivalent DNF expression, it could nevertheless be the case that more structured representations, which form deep theories by forming intermediate concept…

FOS: Computer and information sciencesComputer Science - Machine Learninglearning in logicComputer Science - Artificial Intelligencedeep learningmini-batch learningQA75.5-76.95stochastic optimizationMachine Learning (cs.LG)inductive rule learningArtificial Intelligence (cs.AI)Artificial IntelligenceElectronic computers. Computer scienceOriginal Research
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