Variable-Density Self-Organizing Map for Incremental Learning

  • Atsushi Shimada
  • Rin-Ichiro Taniguchi
Schlagworte: Self-Organizing Map, Incremental Learning, DDC: 004 (Data processing, computer science, computer systems)


We propose a new incremental learning method of Self-Organizing Map. Basically, there are three problems in the incremental learning of Self-Organizing Map: 1. depletion of neurons, 2. oblivion of training data previously given, 3. destruction of topological relationship among training samples. Weight-fixed neurons and weight-quasi-fixed neurons are very effective for the second problem. However the other problems still remain. Therefore, we improve the incremental learning method with weight-fixed neurons and weight-quasi-fixed neurons. We solve the problems by introducing a mechanism to increase the number of neurons effectively in the incremental learning process.