An evidence theoretic classification method is proposed in this paper. In order to classify a pattern we consider its neighbours, which are taken as parts of a single source of ev...
Objective Bayesian probability is often defined over rather simple domains, e.g., finite event spaces or propositional languages. This paper investigates the extension of objectiv...
: Jeffrey’s rule of conditioning has been proposed in order to revise a probability measure by another probability function. We generalize it within the framework of the models b...
A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dim...
We propose a novel clustering method that is an extension of ideas inherent to scale-space clustering and support-vector clustering. Like the latter, it associates every data poin...
In an uncertain data set S = (S, p, f) where S is the ground set consisting of n elements, p : S → [0, 1] a probability function, and f : S → R a score function, each element i...
In this paper, we present a family of new algorithms for rate-fidelity optimal packetization of scalable source bit streams with uneven error protection. In the most general settin...