Logical Settings for Concept-Learning

By Luc De Raedt. 1997

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Citations in the corpus

DeepProbLog: Neural Probabilistic Logic Programming (2018, methodology): “We use learning from entailment [De Raedt, 1997], that is, our training examples are pairs (qi, pi) of a query qi and its desired success probability pi, and our goal is to minimize the difference between the predicted and desired success probabilities of the queries. In contrast to the earlier appr”
Probabilistic Inductive Logic Programming (2004, background): “The representation language chosen for representing the examples together with the covers relation determines the inductive logic programming setting [6].”
Machine Learning - The Art and Science of Algorithms that Make Sense of Data (2012, methodology): “De Raedt (2008) provides an excellent recent introduction, and an overview of recent advances and open problems is provided by Muggleton et al. (2012). Flach (1994) gives an introduction to Prolog and also provides high-level implementations of some of the key techniques in inductive logic programmi”
Margin-based first-order rule learning (2007, background): “First of all, we presume the “learning from interpretations” setting (De Raedt 1997).”
Integrating Guidance into Relational Reinforcement Learning (2004, background): “Examples in relational learning most commonly consist of specific (or ground) facts in the setting of learning from entailment; De Raedt (1997) or sets of facts or interpretations in the setting of learning from interpretations (De Raedt and Džeroski, 1994).”