Publication 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

journal article - other

Language

en

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

RIV year

2014

Released

31.12.2013

Publisher

NEUVEDEN

Location

NEUVEDEN

Pages from

265

Pages to

273

Pages count

10

URL

Documents

BibTex


@article{BUT111438,
  author="Pavla {Vrábelová} and Petr {Škoda} and Jaroslav {Vážný}",
  title="Classification of Spectra of Emission Line Stars Using Machine Learning Techniques",
  annote="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).",
  address="NEUVEDEN",
  chapter="111438",
  edition="NEUVEDEN",
  howpublished="print",
  institution="NEUVEDEN",
  number="3",
  volume="11",
  year="2013",
  month="december",
  pages="265--273",
  publisher="NEUVEDEN",
  type="journal article - other"
}