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- W2147714594 abstract "Relaxation labeling processes are a class of mechanisms that solve the problem of assigning labels to objects in a manner that is consistent with respect to some domain-specific constraints. We reformulate this using the model of a team of learning automata interacting with an environment or a high-level critic that gives noisy responses as to the consistency of a tentative labeling selected by the automata. This results in an iterative linear algorithm that is itself probabilistic. Using an explicit definition of consistency we give a complete analysis of this probabilistic relaxation process using weak convergence results for stochastic algorithms. Our model can accommodate a range of uncertainties in the compatibility functions. We prove a local convergence result and show that the point of convergence depends both on the initial labeling and the constraints. The algorithm is implementable in a highly parallel fashion." @default.
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- W2147714594 date "1986-03-01" @default.
- W2147714594 modified "2023-09-27" @default.
- W2147714594 title "Relaxation Labeling with Learning Automata" @default.
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- W2147714594 doi "https://doi.org/10.1109/tpami.1986.4767779" @default.
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