Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-N...
Dror Baron, Shriram Sarvotham, Richard G. Baraniuk
We propose a novel algorithm for extracting the structure of a Bayesian network from a dataset. Our approach is based on generalized conditional entropies, a parametric family of e...
We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of ...
Constantinos Constantinopoulos, Michalis K. Titsia...
Approximate text search is a basic technique to handle recognized text that contains recognition errors. This paper proposes an approximate string search for recognized text using...
In this paper we propose an alternative interpretation of Bayesian learning based on maximal evidence principle. We establish a notion of local evidence which can be viewed as a c...