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VRÁBEL, J. KÉPEŠ, E. POŘÍZKA, P. KAISER, J.
Original Title
Artificial Neural Networks for Classification
Type
book chapter
Language
English
Original Abstract
Laser‐induced plasma emission spectra contain vast amounts of information. Yet, the discovery of appropriate patterns in laser‐induced breakdown spectra is paramount to reliably performing both quantitative and qualitative analysis. This chapter provides a brief introduction of artificial neural network (ANN) classification models, which have recently become a fundamental part of most pattern recognition toolboxes. The working principles of ANNs are discussed along with the most frequently used architecture types. Special attention is given to the training process of ANNs with the aim of aiding the reader's troubleshooting capabilities. Moreover, some of the potential perils of ANN models are presented. Namely, the risk of overtraining is addressed extensively while providing several potential ailments. Lastly, a comprehensive overview of the applications of ANNs for the classification of LIBS spectra is provided and a few exemplary use‐cases of ANN classifiers are discussed in detail.
Keywords
Chemometrics, Artificial Neural Networks, Classification, Laser-induced breakdown spectroscopy
Authors
VRÁBEL, J.; KÉPEŠ, E.; POŘÍZKA, P.; KAISER, J.
Released
6. 10. 2022
ISBN
978-1-119-75958-4
Book
Chemometrics and Numerical Methods in LIBS
Edition
1
Pages from
213
Pages to
240
Pages count
28
URL
https://www.wiley.com/en-it/Chemometrics+and+Numerical+Methods+in+LIBS-p-9781119759584
BibTex
@inbook{BUT179512, author="Jakub {Vrábel} and Erik {Képeš} and Pavel {Pořízka} and Jozef {Kaiser}", title="Artificial Neural Networks for Classification", booktitle="Chemometrics and Numerical Methods in LIBS", year="2022", series="1", pages="213--240", doi="10.1002/9781119759614.ch9", isbn="978-1-119-75958-4", url="https://www.wiley.com/en-it/Chemometrics+and+Numerical+Methods+in+LIBS-p-9781119759584" }