Input Selection and Regression using the SOM
نویسندگان
چکیده
This paper presents a global methodology to build a nonlinear regression when the number of available samples is small compared to the number of inputs. The task is divided in two parts: selection of the best inputs and construction of the approximator. A first SOM is used to compute clean correlations between the inputs and the output. A second SOM is built to link the output to the selected inputs. The good performances of this methodology are illustrated on a spectrometric dataset.
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