The effect of quantization of prior probabilities in a collection of distributed Bayesian binary hypothesis testing problems over which the priors themselves vary is studied. In ...
Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterio...
This paper studies quantization error in the context of Matching Pursuit coded streams and proposes a new coefficient quantization scheme taking benefit of the Matching Pursuit pr...
Abstract. A black box method was recently given that solves the problem of online approximate matching for a class of problems whose distance functions can be classified as being ...
Abstract. Approximate string matching is a fundamental and challenging problem in computer science, for which a fast algorithm is highly demanded in many applications including tex...