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- W321661710 abstract "Abstract : In the first year of funding our research focused on classification tasks under uncertainty, with special emphasis on propagating beliefs in a system containing a mixture of categorical and probabilistic relationships. Previously available techniques were able to handle taxonomic trees, where the main relationship is ISA--i.e., class membership. The knowledge involved in object recognition contains non-binary relationships (e.g., IN-BETWEEN) arranged in non-decomposable structures. The difficulties encountered stem from the incompleteness of the model. In other words, we normally have information about the relationship between a variable and each of its neighbors but not between a variable and all of its neighbors taken together. This precludes the construction of a complete Bayesian model. To overcome this difficulty, we have developed a new formulation of the Dempster-Shafer theory in terms of a static constraint-networks (representing the stable knowledge) bombarded by randomly fluctuating constraints, (representing uncertain evidence) (Appendix I). We have also devised a scheme for computing Dempster-Shafer belief functions in that model, using Assumption-Based Truth Maintenance Systems (ATMS) and Incidence Calculus (Appendix I)." @default.
- W321661710 created "2016-06-24" @default.
- W321661710 creator A5022060193 @default.
- W321661710 date "1988-09-30" @default.
- W321661710 modified "2023-09-27" @default.
- W321661710 title "Distributed Inferencing for Classification" @default.
- W321661710 doi "https://doi.org/10.21236/ada208182" @default.
- W321661710 hasPublicationYear "1988" @default.
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