Abstract. This paper is concerned with generalization issues for a decision tree learner for structured data called Alkemy. Motivated by error bounds established in statistical lea...
In this paper we propose an original approach to apply data mining algorithms, namely decision tree-based methods, taking into account not only the size of processed databases but ...
Abstract A dynamic programming algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, and shown to be very effective for low dimensional data ...
We investigate an inherent limitation of top-down decision tree induction in which the continuous partitioning of the instance space progressively lessens the statistical support o...
Abstract. We present a logical approach to graph theoretical learning that is based on using alphabetic substitutions for modelling graph morphisms. A classi ed graph is represente...