In [3], we introduced a framework for querying and updating probabilistic information over unordered labeled trees, the probabilistic tree model. The data model is based on trees ...
The paper is an overview of a recently developed compilation data structure for graphical models, with specific application to constraint networks. The AND/OR Multi-Valued Decision...
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
When different subsamples of the same data set are used to induce classification trees, the structure of the built classifiers is very different. The stability of the structure of ...
Abstract. Human-based genetic algorithms are powerful tools for organizational modeling. If we enhance them using chance discovery techniques, we obtain an innovative approach for ...