Abstract
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 α-stable model.KeywordsProbability Density FunctionBayesian ApproachGaussian ModelAutonomous Underwater VehicleBelief FunctionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
