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Variable selection and neural networks. Edition No. 1
VDM Publishing House, April 2008, Pages: 168
This book focuses particularly on the application of chemometrics in the field of analytical chemistry. In infrared spectroscopy for instance, chemometrics consists in the prediction of a quantitative variable (the obtention of which is delicate, requiring a chemical analysis and a qualified operator), such as the concentration of a component present in the studied product from spectral data measured on various wavelengths or wavenumbers. In this book the author proposes a methodology in the field of chemometrics to handle the spectrophotometric data which are often represented in high dimension. To handle these data, a new incremental method (step-by-step) is proposed for the selection of spectral data using linear and non-linear regression. The author proposes, also, to improve the previous method by a judicious choice of the first selected variable, which has a very important influence on the final performances of the prediction. The idea is to use a measure of the mutual information between the independent and dependent variables to select the first one; then the previous incremental method (step-by-step) is used to select the next variables.
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