0000000000203049
AUTHOR
Geir Horn
showing 2 related works from this author
Solving Multiconstraint Assignment Problems Using Learning Automata
2010
Published version of an article in the journal: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works This paper considers the NP-hard problem of object assignment with respect to multiple constraints: assigning a set of elements (or objects) into mutually exclusive classes (or groups), where the elements which are ldquosimilarrdquo to ea…
Stochastic discretized learning-based weak estimation: a novel estimation method for non-stationary environments
2016
The task of designing estimators that are able to track time-varying distributions has found promising applications in many real-life problems.Existing approaches resort to sliding windows that track changes by discarding old observations. In this paper, we report a novel estimator referred to as the Stochastic Discretized Weak Estimator (SDWE), that is based on the principles of discretized Learning Automata (LA). In brief, the estimator is able to estimate the parameters of a time varying binomial distribution using finite memory. The estimator tracks changes in the distribution by operating a controlled random walk in a discretized probability space. The steps of the estimator are discre…