A comparison between a Bayesian approach and a method based on continuous belief functions for pattern recognition

International audience
The theory of belief functions in discrete domain has been employed with success for pattern recognition. However, the Bayesian approach performs well provided that once the probability density functions are well estimated. Recently, the theory of belief functions has been more and more developed to the continuous case. In this paper, we compare results obtained by a Bayesian approach and a method based on continuous belief functions to characterize seabed sediments. The probability density functions of each feature of seabed sediments are unimodal and estimated from a Gaussian model and compared with an a-stable model.