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Relaxation Labeling with Learning Automata
February 1986 (vol. 8 no. 2)
pp. 256-268
Mandayam A. L. Thathachar, Department of Electrical Engineering, Indian Institute of Science, Bangalore 560012, India.
P. S. Sastry, Department of Electrical Engineering, Indian Institute of Science, Bangalore 560012, India.
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.
Citation:
Mandayam A. L. Thathachar, P. S. Sastry, "Relaxation Labeling with Learning Automata," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 2, pp. 256-268, Feb. 1986, doi:10.1109/TPAMI.1986.4767779
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