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- W100165632 abstract "Spectroscopic techniques are only as powerful as the information that can be extracted from the resulting spectral data. Machine learning is the study of techniques for the automated extraction of information from raw data. Proper application of machine learning to spectral data allows users to make decisions as data are collected, without human-in-the-loop processing. This chapter provides an overview of the application of machine-learning techniques to spectroscopic data. Topics such as data pre-processing, feature selection, classifier development, and cross-validation are discussed in light of the high dimensional data typical of laser spectroscopy." @default.
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- W100165632 date "2014-01-01" @default.
- W100165632 modified "2023-09-25" @default.
- W100165632 title "Multivariate analysis, chemometrics, and machine learning in laser spectroscopy" @default.
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- W100165632 doi "https://doi.org/10.1533/9780857098733.1.125" @default.
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