Target Identification Based on the Transferable Belief Model Interpretation of Dempster–Shafer Model

Delmotte, François; Smets, Philippe

doi:10.1109/tsmca.2004.826266

Cited by 109 publications

(70 citation statements)

References 19 publications

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“…The Bayes' theorem of probability theory is replaced in the framework of belief function by the Generalized Bayesian Theorem (GBT), [39,33,40,41]. This theorem provides a way to reverse conditional belief functions without any prior knowledge.…”

Section: Generalized Bayesian Theoremmentioning

confidence: 99%

Learning from partially supervised data using mixture models and belief functions

Côme

Oukhellou

Denux

et al. 2009

Pattern Recognition

108

110

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This paper addresses classification problems in which the class membership of training data is only partially known. Each learning sample is assumed to consist in a feature vector xi ∈ X and an imprecise and/or uncertain "soft" label mi defined as a Dempster-Shafer basic belief assignment over the set of classes. This framework thus generalizes many kinds of learning problems including supervised, unsupervised and semi-supervised learning. Here, it is assumed that the feature vectors are generated from a mixture model. Using the Generalized Bayesian Theorem, an extension of Bayes' theorem in the belief function framework, we derive a criterion generalizing the likelihood function. A variant of the EM algorithm dedicated to the optimization of this criterion is proposed, allowing us to compute estimates of model parameters. Experimental results demonstrate the ability of this approach to exploit partial information about class labels.

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Section: Generalized Bayesian Theoremmentioning

confidence: 99%

Learning from partially supervised data using mixture models and belief functions

Côme

Oukhellou

Denux

et al. 2009

Pattern Recognition

108

110

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show abstract

“…4) with parameter s = 0.9. To exploit the EvHMM, we first use the GBT to transform the likelihoods generated by each detector into belief function distributions [8,20]. These beliefs are then used in the Viterbi decoder.…”

Section: Illustration On Fault Diagnosismentioning

confidence: 99%

Contribution of belief functions to hidden markov models with an application to fault diagnosis

Ramasso¹

2009

2009 IEEE International Workshop on Machine Learning for Signal Processing

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“…Unspecificity is not adequately represented by probability functions, as illustrated in [5] in the context of data association in multi-object classification. The belief function theory, on the contrary, handles unspecifity in a correct manner, thus being able to fuse sensor reports at different levels of granularity [6].…”

mentioning

confidence: 99%

Target identification using belief functions and implication rules

Ristić

Smets

2005

IEEE Trans. Aerosp. Electron. Syst.

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The paper presents the theoretical basis of data fusion for the purpose of target identification using the belief function theory. The key feature of this paper is that we allow the knowledge sources to supply their information in the form of the implication rules. The paper describes how these rules can be elegantly handled within the framework of the belief function theory. A small scale practical example for target identification is worked out in details in order to clarify the theory for future users.

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Target Identification Based on the Transferable Belief Model Interpretation of Dempster–Shafer Model

Cited by 109 publications

References 19 publications

Learning from partially supervised data using mixture models and belief functions

Learning from partially supervised data using mixture models and belief functions

Contribution of belief functions to hidden markov models with an application to fault diagnosis

Target identification using belief functions and implication rules

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