Improvement of Memory-Based Estimation by Distributed Neighborhood Selection

Abstract

Memory-based estimation is a method to locally estimate a nonlinear function by using massive data stored in a database. It generally consumes a considerable amount of time on arithmetic processing. To make the memory-based estimation method more practical, it is necessary to reduce the computation time. In this paper, we improve memory-based estimation by distributively executing the estimation algorithm.