Building adaptive basis functions with a continuous self-organizing map

Author(s): Campos, M.M. | Carpenter, G.A. |

Year: 2000

Citation: Neural Processing Letters, 11, 59-78.

Abstract: This paper introduces CSOM, a continuous version of the Self-Organizing Map (SOM). The CSOM network generates maps similar to those created with the original SOM algorithm but, due to the continuous nature of the mapping, CSOM outperforms the SOM on function approximation tasks. CSOM integrates self-organization and smooth prediction into a single process. This is a departure from previous work that required two training phases, one to self-organize a map using the SOM algorithm, and another to learn a smooth approximation of a function. System performance is illustrated with three examples.

Topics: Machine Learning, Models: Self Organizing Maps,

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