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This paper describes a problem of distribution free learning theory wherein a collection of n agents make independent and identically distributed observations of an unknown function and seek consensus in their construction of an optimal estimate. The learning objective is expressed in terms of an error defined over a reproducing kernel Hilbert spaces (RKHS) and in terms of a multiplier that enforces...
In this paper we demonstrate that the support vector tracking (SVT) framework first proposed by Avidan is equivalent to the canonical Lucas-Kanade (LK) algorithm with a weighted Euclidean norm. From this equivalence we empirically demonstrate that in many circumstances the canonical SVT approach is unstable, and characterize these circumstances theoretically. We then propose a novel ldquononpositive...
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