A method to induce bayesian networks from data to overcome some limitations of other learning algorithms is proposed. One of the main features of this method is a metric to evalua...
Many interesting analyses for constraint logic-based languages are aimed at the detection of monotonic properties, that is to say, properties that are preserved as the computation...
Temporal reasoning is widely used within both Computer Science and A.I. However, the underlying complexity of temporal proof in discrete temporal logics has led to the use of simp...
We propose an extension of functional logic languages that allows the definition of operations with patterns containing other defined operation symbols. Such “function patterns...
We consider the supervised learning of a binary classifier from noisy observations. We use smooth boosting to linearly combine abstaining hypotheses, each of which maps a subcube...