Abstract
Cognitive development refers to the ability of a system to gradually acquire knowledge through experiences during its existence. As a consequence, the learning strategy should be represented as an integrated, online process that aims to build a model of the "world" and a continuous update of this model. Considering as reference the Modal Model of Memory introduced by Atkinson and Schiffrin, we propose an online learning algorithm for cognitive systems design. The incremental part of the algorithm is responsible of updating existing information or creating new data categories and the decremental part, to efficiently evaluate the system's performance facing partial or total loss of data. The proposed algorithm has been applied to the face recognition problem. More generally, the current approach can be extended to large-scale classification problems, to limit the memory requirements for optimal data representation and storage.
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