activity
20082013
most citedA new generalization of the proportional conflict redistribution rule stable in terms of decision

68 citations · 179 across the 7 of their papers we have counts for

collaborators

8 papers

cs.IT2013★ 37 cited

Controling the number of focal elements

Christophe Osswald

A basic belief assignment can have up to 2^n focal elements, and combining them with a simple conjunctive operator will need O(2^2n) operations. This article proposes some techniqu…

cs.AI2011★ 18 cited

Contradiction measures and specificity degrees of basic belief assignments

Florentin Smarandache, Arnaud Martin, Christophe Osswald

In the theory of belief functions, many measures of uncertainty have been introduced. However, it is not always easy to understand what these measures really try to represent. In t…

cs.AI2009★ 48 cited

General combination rules for qualitative and quantitative beliefs

Arnaud Martin, Christophe Osswald, Jean Dezert +1

Martin and Osswald \cite{Martin07} have recently proposed many generalizations of combination rules on quantitative beliefs in order to manage the conflict and to consider the spec…

cs.CV2008★ 8 cited

Generalized proportional conflict redistribution rule applied to Sonar imagery and Radar targets classification

Arnaud Martin, Christophe Osswald

In this chapter, we present two applications in information fusion in order to evaluate the generalized proportional conflict redistribution rule presented in the chapter \cite{Mar…

cs.CV2008

Experts Fusion and Multilayer Perceptron Based on Belief Learning for Sonar Image Classification

Arnaud Martin, Christophe Osswald

The sonar images provide a rapid view of the seabed in order to characterize it. However, in such as uncertain environment, real seabed is unknown and the only information we can o…

cs.CV2008

Human expert fusion for image classification

Arnaud Martin, Christophe Osswald

In image classification, merging the opinion of several human experts is very important for different tasks such as the evaluation or the training. Indeed, the ground truth is rare…