This paper proposes an original inhomogeneous restoration (deconvolution) model under the Bayesian framework. In this model, regularization is achieved, during the iterative resto...
We develop, analyze, and test a training algorithm for support vector machine classifiers without offset. Key features of this algorithm are a new, statistically motivated stoppi...
—We consider distributed iterative algorithms for the averaging problem over time-varying topologies. Our focus is on the convergence time of such algorithms when complete (unqua...
Angelia Nedic, Alexander Olshevsky, Asuman E. Ozda...
Average consensus and gossip algorithms have recently received significant attention, mainly because they constitute simple and robust algorithms for distributed information proc...
We propose abc-boost (adaptive base class boost) for multi-class classification and present abc-mart, an implementation of abcboost, based on the multinomial logit model. The key ...