Jukka Hekanaho

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We describe a GA-based concept learning/theory revision system DOGMA and discuss how it can be applied to relational learning. The search for better theories in DOGMA is guided by a n o vel tness function that combines the minimal description length and information gain measures. To show the eecacy of the system we compare it to other learners in three(More)
We describe an application of DOGMA, a GA-based theory revision system, to MDL-based rule enhancement in supervised concept learning. The system takes as input classiication data and a rule-based classiication theory, produced by some rule-based learner, and builds a second, hopefully more accurate , model of the data. Unlike most theory revision systems(More)
We describe how proof rules for three advanced reenement features are mechanically veriied using the HOL theorem prover. These features are data reenement, backwards data reenement and superposition reenement of initialised loops. We also show how applications of these proof rules to actual program reenement can be checked using the HOL system, with the HOL(More)
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