Simon Alan Rawles

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Propositionalization has already been shown to be a promising approach for robustly and effectively handling relational data sets for knowledge discovery. In this paper, we compare up-to-date methods for propositionalization from two main groups: logic-oriented and databaseoriented techniques. Experiments using several learning tasks — both ILP benchmarks(More)
We describe an initiative under way at Warwick to provide a technical foundation for computer-aided learning and computer-assisted assessment tools, which allows a rich dialogue sensitive to individual students' response patterns. The system distinguishes between dialogues for individual problems and the linking of problems. This enables a subject(More)
We consider the problem of eliminating redundant Boolean features for a given data set, where a feature is redundant if it separates the classes less well than another feature or set of features. Lavrač et al. proposed the algorithm REDUCE that works by pairwise comparison of features, i.e., it eliminates a feature if it is redundant with respect to(More)
Stochastic logic programs combine ideas from probabilistic<lb>grammars with the expressive power of definite clause logic; as such they<lb>can be considered as an extension of probabilistic context-free grammars.<lb>Motivated by an analogy with learning tree-bank grammars, we study<lb>how to learn stochastic logic programs from proof-trees. Using(More)
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