Batch-Learning Self-Organizing Map with false-neighbor degree between neurons

Abstract— This study proposes a Batch-Learning SelfOrganizing Map with False-Neighbor degree between neurons (called BL-FNSOM). False-neighbor degrees are allocated between adjacent rows and adjacent columns of BL-FNSOM. The initial values of all of the false-neighbor degrees are set to zero, however, they are increased with learning, and the falseneighbor degrees act as a burden of the distance between map nodes when the weight vectors of neurons are updated. BLFNSOM changes the neighborhood relationship more flexibly according to the situation and the shape of data although using batch learning. We apply BL-FNSOM to some input data and confirm that FN-SOM can obtain a more effective map reflecting the distribution state of input data than the conventional Batch-Learning SOM.

• Type: Conference
• Year: 2008
• Where: IJCNN
• Authors: Haruna Matsushita, Yoshifumi Nishio
• Keywords: Artificial Intelligence, Batch-Learning SelfOrganizing Map, False-neighbor Degrees, Falseneighbor Degrees Act, IJCNN 2008