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We present a novel neural learning architecture for regression data analysis. It combines, at the high level, a self-organizing map (SOM) structure, and, at the low level, a multilayer perceptron at each unit of the SOM structure. The goal is to build a clusterwise regression model, that is, a model recognizing several clusters in the data, where the dependence between predictors and response is variable (typically within some parametric range) from cluster to cluster. The proposed algorithm, called SOMwise Regression, follows closely in the spirit of the standard SOM learning algorithm and has performed satisfactorily on various test problems. © 2011 Springer-Verlag London Limited.

Original publication

DOI

10.1007/s00521-011-0536-3

Type

Journal article

Journal

Neural Computing and Applications

Publication Date

01/09/2012

Volume

21

Pages

1229 - 1241