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An adaptive evidence structure for Bayesian recognition of 3D objects
2015
Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication - IMCOM '15
Classification of an object under various environmental conditions is a challenge for developing a reliable service robot. In this work, we show problems of using simple Naïve Bayesian classifier and propose a Tree-Augmented Naïve (TAN) Bayesian Networkbased classifier. We separate feature space into binary TRUE/FALSE regions which allows us to drive Bayesian inference prior conditional probabilities from statistical database. We go further using TRUE/FALSE regions to estimate expected
doi:10.1145/2701126.2701160
dblp:conf/icuimc/NaguibL14
fatcat:ewbhznhkpzhilccy63q2ll3t3m