Univariate decision tree algorithms are widely used in Data Mining because (i) they are easy to learn (ii) when trained they can be expressed in rule based manner. In several applications mainly including Data Mining, the dataset to be learned is very large. In those cases it is highly desirable to construct univariate decision trees in reasonable time. This may be accomplished by parallelizing...

Based on a constructive representation, which distinguishes between a skewing mechanism P and an underlying symmetric distribution F , we introduce two flexible classes of distributions. They are generated by nonparametric modelling of either P or F . We examine properties of these distributions and consider how they can help us to identify which aspects of the data are badly captured by simple...

We formalize the notion of approximate GCD for univariate poly-nomials given with limited accuracy and then address the problem of its computation. Algebraic concepts are applied in order to provide a solid foundation for a numerical approach. We exhibit the limitations of the euclidean algorithm through experiments, show that existing methods only solve part of the problem and assert its worst...