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IJCNN
2008
IEEE

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

13 years 10 months ago
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.
Haruna Matsushita, Yoshifumi Nishio
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where IJCNN
Authors Haruna Matsushita, Yoshifumi Nishio
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