An algorithm for fast and reliable ESOM learning

The training of Emergent Self-organizing Maps (ESOM ) with large datasets can be a computationally demanding task. Batch learning may be used to speed up training. It is demonstrated here, however, that the representation of clusters in the data space on maps trained with batch learning is poor compared to sequential training. This effect occurs even for very clear cluster structures. The k-batch learning algorithm is preferrable, because it creates the same quality of representation as sequential learning but maintains important properties of batch learning that can be exploited for speedup.

• Type: Conference
• Year: 2006
• Where: ESANN
• Authors: Mario Nöcker, Fabian Mörchen, Alfred Ultsch
• Keywords: Batch Learning, Emergent Self-Organizing Maps, ESANN 2006, ESANN 2007, K-batch Learning Algorithm