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en:research [2011/11/21 17:13] – created sdesterc | en:research [2015/05/22 09:37] – sdesterc | ||
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- | My research mainly | + | My research mainly |
- | Inside | + | Inside |
====== Practical uncertainty representations ====== | ====== Practical uncertainty representations ====== | ||
- | Work with D. Dubois, M. Troffaes, E. Miranda, L. Utkin, E. Chojancki | + | Work (mainly) benefiting from collaborations and discussions |
There exist many practical representations in imprecise probability theories, including possibility distributions, | There exist many practical representations in imprecise probability theories, including possibility distributions, | ||
- | Aside establishing new relations between the properties of several models (i.e. clouds, p-boxes, possibility distributions), | + | Aside establishing new relations between the properties of several models (i.e. clouds, p-boxes, possibility distributions), |
- | Our current focus is to link this model with other models (such as info-gap theory) that use the idea of nested sets to propose solutions to statistical problems. | + | Our latest research on the topic include the specification of p-boxes limitations, |
====== Information fusion and combination ====== | ====== Information fusion and combination ====== | ||
- | Work with D. Dubois, P. Buche, B. Charnomordic, | + | Work (mainly) benefiting from collaborations and discussions |
- | Merging information from multiple sources is a common | + | Merging information from multiple sources is a recurring |
For practical purposes, we have proposed to use the notion of maximal coherent subsets as a way to deal with conflict among sources, and have applied it to the problem of estimating source reliability form meta-information. | For practical purposes, we have proposed to use the notion of maximal coherent subsets as a way to deal with conflict among sources, and have applied it to the problem of estimating source reliability form meta-information. | ||
- | Our current focus on this area is to apply information fusion | + | Our current focus in this area concerns the characterization of inconsistency |
====== Uncertainty propagation and (in)dependence modelling ====== | ====== Uncertainty propagation and (in)dependence modelling ====== | ||
- | Work with D. Dubois, G. De Cooman E. Chojnacki, J. Baccou | + | Work (mainly) benefiting from collaborations and discussions |
- | A common problem is how to propagate uncertainty analysis in various models. Most of my research in this domain has concerned the propagation of uncertainty model through deterministic | + | How to propagate uncertainty analysis in various models |
- | Related problems are how to model independence to obtain joint models (and how to compute with such latter models), or how to simulate a given imprecise probabilistic model. | + | Related problems are how to model independence to obtain |
+ | Some of our recent research also deals with the problem of how to efficiently evaluate the reliability when the component reliabilities are uncertain and modelled by imprecise probabilistic knowledge (more speicifically belief functions). | ||
- | ====== Practical applications ====== | ||
- | Work with P. Buche, B. Charnomordic, | ||
- | We have applied ideas coming from imprecise probability theory and more generally concerning uncertainty handling to a number of frameworks, including: | + | ====== Learning problems ====== |
- | * Flexible querying in data bases (P. Buche, V. Guillard) | + | |
- | * Signal filtering with kernels (O. Strauss) | + | |
- | * Knowledge Engineering (B. Charnomordic, | + | |
- | * Risk analysis and robust design (E. Chojancki, V. Guillard) | + | |
+ | Work (mainly) benefiting from collaborations and discussions with B. Quost, T. Denoeux, B. Ben Yaghlane, N. Sutton-Charani, | ||
+ | Outside of extending some classical classifiers (k-NN methods, Naïve networks) to imprecise probabilistic settings, our work currently focuses on the combination of classifiers, | ||
+ | One of our current favorite field of investigation is the so-called binary decomposition, | ||
+ | ====== Applications ====== | ||
- | + | Work (mainly) benefiting from collaborations and discussions with P. Buche, B. Charnomordic, | |
+ | |||
+ | We have applied ideas coming from imprecise probability theories and more generally concerning uncertainty handling to a number of frameworks, including: | ||
+ | |||
+ | * Flexible querying in data bases (P. Buche, V. Guillard) | ||
+ | * Signal filtering with kernels (O. Strauss, F. Comby) | ||
+ | * Knowledge Engineering (B. Charnomordic, | ||
+ | * Risk analysis and robust design (E. Chojancki, V. Guillard, M. Sallak) | ||
+ | * Process modelling (C. Baudrit) | ||
+ | * Virtual training (I. Thouvenin) |