{"product_id":"9783836495042","title":"Variable Selection and Neural Networks","description":"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.\u003cbr\u003eIn 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.","brand":"VDM Verlag Dr. Mueller E.K.","offers":[{"title":"Default Title","offer_id":47065924206832,"sku":"9783836495042","price":89.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9783836495042_p0.jpg?v=1763687875","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9783836495042","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}