Brain-inspired self-organizing model for incremental learning
Kasun Gunawardana, Jayantha Rajapakse, Damminda Alahakoon · 2013
Machine learning techniques which are involved in knowledge extraction from stationary datasets have been becoming inefficient due to the dynamic nature of contemporary data spaces. Hence, machine learning research constantly investigates incremental learning techniques to address this requirement. However, it is always a challenge to uncover useful information incrementally from a non-stationary input space because of the complexity an algorithm introduces to counter the stability-plasticity dilemma. In order to facilitate this demand a learning model is proposed using the self-organization and competitive learning strategy. Moreover, an algorithm which is implemented based on the proposed model is also presented with the experimental results to prove the validity of the proposed learning model in a non-stationary context.