We consider a general class of regularization methods which learn a vector of parameters on the basis of linear measurements. It is well known that if the regularizer is a nondecr...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
This paper presents a nonparametric approach to labeling
of local image regions that is inspired by recent developments
in information-theoretic denoising. The chief novelty
of ...
Recently, "epitomes" were introduced as patch-based probability models that are learned by compiling together a large number of examples of patches from input images. In...
Forecasting future events based on historic data is useful in many domains like system management, adaptive query processing, environmental monitoring, and financial planning. We...
Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination ...