Searching for authors named "Philippe Smets" – sorted by Relevance.
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Analyzing the Combination of Conflicting Belief Functions
- …We consider uncertain data which uncertainty is represented by belief functions and that must be combined. The result of the combination of the belief functions can be partially conflictual. Initially Shafer proposed Dempster’s rule of combination where the conflict is reallocated proportionally amo…
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Decision Making in a Context where Uncertainty is Represented by Belief Functions.
- …A quantified model to represent uncertainty is incomplete if its use in a decision environment is not explained. When belief functions were first introduced to represent quantified uncertainty, no associated decision model was proposed. Since then, it became clear that the belief functions meani…
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Quantified epistemic possibility theory seen as an hyper cautious Transferable Belief Model
- …We provide a semantic for the values given to possibility measures. It is based on the semantic of the transferable belief model, itself based on the same approach as used for subjective probabilities. Besides we explain how the conjunctive combination of two possibility measures corresponds to the …
- Cited by 9 (0 self) – Add To MetaCart
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Data Fusion in the Transferable Belief Model.
- …When Shafer introduced his theory of evidence based on the use of belief functions, he proposed a rule to combine belief functions induced by distinct pieces of evidence. Since then, theoretical justifications of this socalled Dempster's rule of combination have been produced and the meaning of dist…
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Target Identification Based on the Transferable Belief Model Interpretation of Dempster-Shafer Model. Pars I: Methodology
- …This paper explains how multisensor data fusion and target identification can be performed within the transferable belief model, a model for the representation of quantified uncertainty based on belief functions. The paper is presented in two parts: methodology and application. In this part, we pres…
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Generating Explanations for Evidential Reasoning
- …In this paper, we present two methods to provide explanations for reasoning with belief functions in the valuation-based systems. One approach, inspired by Strat's method, is based on sensitivity analysis, but its computation is simpler thus easier to implement than Strat's. The other one is t…
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Data Association in Multi-Target Detection Using the Transferable Belief Model.
- …In the transferable belief model, a model for the quantified representation of beliefs, some masses can be allocated to the empty set. It reflects the conflict between the sources of information. This quantified conflict can be used in order to solve the problem of data association in a multitarg…
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Some Strategies for Explanations in Evidential Reasoning
- …We present two methods to provide explanations for reasoning with belief functions. One approach, inspired by Strat's method, is based on sensitivity analysis, but its computation is simpler thus easier to implement than Strat's. The other approach is to examine the impact of each piece of eviden…
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Kalman Filter and Joint Tracking and Classification in the TBM Framework
- …The paper presents an approach to joint tracking and classification based on belief functions as understood in the transferable belief model (TBM). For the tracking phase, a Kalman filter in the TBM framework is derived. This filter is essentially the same as the classical Kalman filter with a di#us…
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Learning From an Imprecise Teacher: Probabilistic and Evidential Approaches
- …. A type of learning problem is considered, in which the class of training examples is only partially specied. Two approaches to such problems are described: the maximum likelihood approach, in which a probabilistic model relating the imprecise label to the true class is postulated, and the Transfer…
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