Adaptive Mechanisms for Classification Problems with Drifting Data
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Incremental learning with multi-level adaptation
Neurocomputing
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Adaptivity in neural networks aims at equipping learning algorithms with the ability to self-update as new training data becomes available. In many application, data arrives over long periods of time, hence the traditional one-shot training phase cannot be applied. The most appropriate training methodology in such circumstances is incremental learning (IL). The present paper introduces a new IL algorithm dedicated to classification problems. The basic idea is to incrementally generate prototyped categories which are then linked to their corresponding classes. Numerical simulations show the performance of the proposed algorithm.