A new learning algorithm for incremental self-organizing maps
Yann Prudent, Abdel Ennaji · 2005
Abstract. An incremental and Growing network model is introduced which is able to learn the topological relations in a given set of input vectors by means of a simple Hebb-like learning rule. First an overview of the most known models of Self-Organizing Maps (SOM) is given. Then we propose a new algorithm for a SOM which can learn new input data (plasticity) without degrading the previously trained network and forgetting the old input data (stability). We report the validation of this model on extensive experiments using a synthetic problem and the handwriting digit recognition problem over a portion of the NIST database. 1