Publication detail

Artificial Neural Networks for Classification

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

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"
}