Regression function and noise variance tracking methods for data streams with concept drift - Jaworski, Maciej (Zielona Góra: Uniwersytet Zielonogórski)
Two types of heuristic estimators based on Parzen kernels are presented. They are able to estimate the regression function in an incremental manner. The estimators apply two techniques commonly used in concept-drifting data streams, i.e., the forgetting factor and the sliding window. The methods are applicable for models in which both the function and the noise variance change over time. Although nonparametric methods based on Parzen kernels were previously successfully applied in the literature to online regression function estimation, the problem of estimating the variance of noise was generally neglected.
Keywords: data streams; concept drift; Parzen kernels; regression; variance estimation