We address the problem of nding useful regions for two-dimensional association rules and decision trees. In a previous paper we presented ecient algorithms for computing optimiz...
The quest to nd models usefully characterizing data is a process central to the scienti c method, and has been carried out on many fronts. Researchers from an expanding number of ...
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Ga...
We develop a method to forecast stock keeping unit sales that is accurate, transparent and consistent in handling similar situations. We leverage the marketing literature to define...
Selecting promising queries is the key to effective active learning. In this paper, we investigate selection techniques for the task of learning an equivalence relation where the ...