In this paper, we develop a general classification framework called Kullback-Leibler Boosting, or KLBoosting. KLBoosting has following properties. First, classification is based on the sum of histogram divergences along corresponding global and discriminating linear features. Second, these linear features, called KL features, are iteratively learnt by maximizing the projected Kullback-Leibler d...

this paper considers the search problem, introduced by srivastava cite{sr}. this is a model discrimination problem. in the context of search linear models, discrimination ability of search designs has been studied by several researchers. some criteria have been developed to measure this capability, however, they are restricted in a sense of being able to work for searching only one possible non...

Asymptotic unbiasedness and L2-consistency are established, under mild conditions, for the estimates of Kullback–Leibler divergence between two probability measures in Rd, absolutely continuous with respect to (w.r.t.) Lebesgue measure. These based on certain k-nearest neighbor statistics pair independent identically distributed (i.i.d.) due vector samples. The novelty results is also treating ...