We study the problem of selecting a subset of k random variables from a large set, in order to obtain the best linear prediction of another variable of interest. This problem can ...
Abstract. A novel approach for model set based object segmentation is described. The proposed method enables the using of a model set to guide the object segmentation. The object s...
We study selectivity estimation techniques for set similarity queries. A wide variety of similarity measures for sets have been proposed in the past. In this work we concentrate o...
Marios Hadjieleftheriou, Xiaohui Yu, Nick Koudas, ...
We optimize the full-response diagnostic fault dictionary from a given test set. The smallest set of vectors is selected without loss of diagnostic resolution of the given test se...