A new numerical approach of nding out the full and reduced structural descriptions of an object (image) or class is suggested in the paper. These structural descriptions are based on the Boolean formulas obtained by a learning procedure. The conjunctions for each object at the rst level of generalization are considered as features. At the next level of generalization the relationship between co...

The recognition of named entities is now a well-developed area, with a range of symbolic and machine learning techniques that deliver high accuracy extraction and categorisation of a variety of entity types. However, there are still some named entity phenomena that present problems for existing techniques; in particular, relatively little work has explored the disambiguation of conjunctions app...

In this paper we seek to provide greater automation for formal deductive verification tools working with continuous and hybrid dynamical systems. We present an efficient procedure to check invariance of conjunctions of polynomial equalities under the flow of polynomial ordinary differential equations. The procedure is based on a necessary and sufficient condition that characterizes invariant co...

We study domain filtering algorithms for open constraints, i.e., constraints that are not a priori defined on specific sets of variables. We present an efficient filtering algorithm, achieving set-domain consistency, for open global cardinality constraints. We extend this result to conjunctions of them, in case they are defined on disjoint sets of variables. We also analyze the case when the se...

We introduce a novel framework, called BLOSOM, for mining (frequent) boolean expressions over binary-valued datasets. We organize the space of boolean expressions into four categories: pure conjunctions, pure disjunctions, conjunction of disjunctions, and disjunction of conjunctions. We focus on mining the simplest expressions (theminimal generators) for each class. We also propose a closure op...