General dissimilarity-based learning approaches have been proposed for dissimilarity data sets [11, 10]. They arise in problems in which direct comparisons of objects are made, e....
Statistical shape models have gained widespread use in medical image analysis. In order for such models to be statistically meaningful, a large number of data sets have to be inclu...
Although real-time interactive volume rendering is available even for very large data sets, this visualization method is used quite rarely in the clinical practice. We suspect this...
Peter Kohlmann, Stefan Bruckner, Armin Kanitsar,...
Dynamic Miss-Countingalgorithms are proposed, which find all implication and similarity rules with confidence pruning but without support pruning. To handle data sets with a large...
Shinji Fujiwara, Jeffrey D. Ullman, Rajeev Motwani
Most data integration applications require a matching between the schemas of the respective data sets. We show how the existence of duplicates within these data sets can be exploi...
Abstract. We address the problem of comparing sets of images for object recognition, where the sets may represent arbitrary variations in an object's appearance due to changin...