Operator-adaptive PLS and Ridge models integrate linear preprocessing screening internally via algebraic identities, delivering comparable or better prediction accuracy than exhaustive external search on NIR regression and classification tasks with orders-of-magnitude lower fitting time.
Modern practical convolutional neural networks for multivariate regression: Applications to nir calibration
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Contradictions across CNN studies for Vis-NIR chemometrics are expected outcomes of uncontrolled variables in spectral physics and validation design, motivating a conditional rather than universal design framework.
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Reframing preprocessing selection as model-internal calibration in near-infrared spectroscopy: A large-scale benchmark of operator-adaptive PLS and Ridge models
Operator-adaptive PLS and Ridge models integrate linear preprocessing screening internally via algebraic identities, delivering comparable or better prediction accuracy than exhaustive external search on NIR regression and classification tasks with orders-of-magnitude lower fitting time.
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CNNs for Vis-NIR Chemometrics: From Contradiction to Conditional Design
Contradictions across CNN studies for Vis-NIR chemometrics are expected outcomes of uncontrolled variables in spectral physics and validation design, motivating a conditional rather than universal design framework.