We consider the problem of learning a ranking function, that is a mapping from instances to rankings over a finite number of labels. Our learning method, referred to as ranking by...
This paper presents a method for finding and classifying objects within real-world scenes by using the activity of humans interacting with these objects to infer the object’s i...
Patrick Peursum, Svetha Venkatesh, Geoff A. W. Wes...
Preference learning is a challenging problem that involves the prediction of complex structures, such as weak or partial order relations, rather than single values. In the recent ...
We consider the equivalence problem for labeled Markov chains (LMCs), where each state is labeled with an observation. Two LMCs are equivalent if every finite sequence of observat...
Laurent Doyen, Thomas A. Henzinger, Jean-Fran&cced...
Label fusion strategies are used in multi-atlas image segmentation approaches to compute a consensus segmentation of an image, given a set of candidate segmentations produced by r...
Paul A. Yushkevich, Hongzhi Wang, John Pluta, Bria...