Abstract: In this paper, we propose a prototype-based low-disturbance framework for image class incremental learning, aiming to address catastrophic forgetting and sample imbalance issues when ...
Abstract: In this research paper, we examine classes of decision tables that are closed under attribute (column) removal and changing of decisions associated with rows. For decision tables belonging ...
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