Detail publikačního výsledku

Artificial Neural Networks for Classification

VRÁBEL, J.; KÉPEŠ, E.; POŘÍZKA, P.; KAISER, J.

Originální název

Artificial Neural Networks for Classification

Anglický název

Artificial Neural Networks for Classification

Druh

Kapitola, resp. kapitoly v odborné knize

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Chemometrics, Artificial Neural Networks, Classification, Laser-induced breakdown spectroscopy

Klíčová slova v angličtině

Chemometrics, Artificial Neural Networks, Classification, Laser-induced breakdown spectroscopy

Autoři

VRÁBEL, J.; KÉPEŠ, E.; POŘÍZKA, P.; KAISER, J.

Rok RIV

2023

Vydáno

06.10.2022

ISBN

978-1-119-75958-4

Kniha

Chemometrics and Numerical Methods in LIBS

Edice

1

Strany od

213

Strany do

240

Strany počet

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"
}