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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...
The task of designing estimators that are able to track time-varying distributions has found promising applications in many real-life problems. A particularly interesting family of distributions are the binomial/multiomial distributions. 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...
All of the prototype reduction schemes (PRS) which have been reported in the literature, process time-invariant data to yield a subset of prototypes that are useful in nearest-neighbor-like classification. Although these methods have been proven to be powerful, they suffer from a major disadvantage when they are utilized for applications involving non-stationary data, namely, time varying samples,...
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