A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems

Abstract

We present a biologically inspired architecture for incremental learning that remains resource-efficient even in the face of very high data dimensionalities (>1000) that are typically associated with perceptual problems. In particular, we investigate how a new perceptual (object) class can be added to a trained architecture without retraining, while… (More)
DOI: 10.1007/s12559-016-9389-5

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Cite this paper

@article{Gepperth2016ABI, title={A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems}, author={Alexander Gepperth and Cem Karaoguz}, journal={Cognitive Computation}, year={2016}, volume={8}, pages={924-934} }