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In this paper,a dynamical memory control strategy based on projection technique is proposed for kernel-based online regression.Namely,when an instance is removed from the memory,its contribution will be kept by projecting the regression function onto the subspace expanded instead of throwing it away cheaply.This strategy composed by incremental and decremental controls.To the former,a new example will be added to the memory if it brings a significant change to the regression function,otherwise discarded by the projection technique.The latter is applied when a new instance is added to the memory,or the memory size has reached a predefined budget.The proposed method is analyzed theoretically and its performance is tested on four benchmark data sets.