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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.

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