Publication result detail

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

VRÁBELOVÁ, P.; ŠKODA, P.; VÁŽNÝ, J.

Original Title

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

English Title

Classification of Spectra of Emission Line Stars Using Machine Learning Techniques

Type

Peer-reviewed article not indexed in WoS or Scopus

Original Abstract

The paper deals with classification and clustering of emission-line spectra of Be stars using discrete wavelet transform (DWT), PCA, and support vector machines (SVM).

English abstract

The paper deals with classification and clustering of emission-line spectra of Be stars using discrete wavelet transform (DWT), PCA, and support vector machines (SVM).

Keywords

Be star, stellar spectrum, feature extraction, dimension reduction, discrete wavelet transform, classification, support vector machines (SVM), clustering

Key words in English

Be star, stellar spectrum, feature extraction, dimension reduction, discrete wavelet transform, classification, support vector machines (SVM), clustering

Authors

VRÁBELOVÁ, P.; ŠKODA, P.; VÁŽNÝ, J.

RIV year

2015

Released

31.12.2013

ISBN

1476-8186

Periodical

International Journal of Automation and Computing

Volume

11

Number

3

State

People's Republic of China

Pages from

265

Pages to

273

Pages count

10

URL

https://www.fit.vut.cz/research/publication/10415/

BibTex

@article{BUT111438,
  author="Pavla {Vrábelová} and Petr {Škoda} and Jaroslav {Vážný}",
  title="Classification of Spectra of Emission Line Stars Using Machine Learning Techniques",
  journal="International Journal of Automation and Computing",
  year="2013",
  volume="11",
  number="3",
  pages="265--273",
  issn="1476-8186",
  url="https://www.fit.vut.cz/research/publication/10415/"
}

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