We propose a new approach for learning Bayesian classifiers from data. Although it relies on traditional Bayesian network (BN) learning algorithms, the effectiveness of our approa...
Abstract. Database modeling is based on the assumption of a high regularity of its application areas, an assumption which applies to both the structure of data and the behavior of ...
Hans-Werner Sehring, Sebastian Bossung, Joachim W....
We show how linear typing can be used to obtain functional programs which modify heap-allocated data structures in place. We present this both as a "design pattern" for ...
We present a general theory of Gifford-style type and effect annotations, where effect annotations are sets of effects. Generality is achieved by recourse to the theory of algebra...
We describe an s-expression based syntax-extension framework much like Scheme macros, with a key additional facility: the ability to define static semantics, such as type systems ...