Photometric redshift estimation: An active learning approach

R. Vilalta, E. E. O. Ishida, R. Beck, R. Sutrisno, R.~S. de Souza, A. Mahabal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2017 IEEE Symposium Series on Computational Intelligence (SSCI)
Place of PublicationHonolulu, HI, USA
Number of pages8
ISBN (Electronic)9781538627266
Publication statusPublished - 8 Feb 2018

Publication series

NameIEEE Symposium Series on Computational Intelligence (SSCI)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)


  • astronomical photometry
  • galaxies
  • learning (artificial intelligence)
  • red shift
  • sampling methods
  • Query by Committee approach
  • active learning approach
  • astronomy
  • feature space
  • galaxy distances
  • informative instances
  • machine learning technique
  • observational selection effects
  • photometric filters
  • photometric measurement distributions
  • photometric redshift estimation
  • photometric redshift estimators
  • photometric sample
  • sampling strategy
  • spectroscopic follow-up measurements
  • spectroscopic sample
  • Adaptation models
  • Astronomy
  • Data models
  • Electronic mail
  • Estimation
  • Extraterrestrial measurements
  • Training

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