The Support Vector Machine (SVM) is a powerful tool for classification. We generalize SVM to work with data objects that are naturally understood to be lying on curved manifolds, ...
Suman K. Sen, Mark Foskey, James Stephen Marron, M...
We show the potential for classifying images of mixtures of aggregate, based themselves on varying, albeit well-defined, sizes and shapes, in order to provide a far more effective...
A generalized image model (GIM) is presented. Images are represented as sets of four-dimensional (4D) sites combining position and intensity information, as well as their associat...
In this paper, we propose a novel metric learning method based on regularized moving least squares. Unlike most previous metric learning methods which learn a global Mahalanobis d...
In Proc. of IEEE Conf. on CVPR'2000, Vol.I, pp.222-227, Hilton Head Island, SC, 2000 In many vision applications, the practice of supervised learning faces several difficulti...