The huge efforts made currently by atomic spectroscopists to resolve interferences and optimise instrumental measuring devices to increase accuracy and precision have led to a point where many of the difficulties that need to be solved nowadays cannot be described by simple classical linear regression methods and not even by other advanced linear regression methods. Typical situations where these can fail involve spectral non‐linearities.
This chapter introduces two relatively recent regression methodologies which, in contrast to classical programming, work with rules rather than with well‐defined and fixed algorithms: artificial neural networks (ANNs), a fairly established technique nowadays, and the support vector machine (SVM), which is emerging as a powerful method to perform both classification and regression tasks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.