@inproceedings{6727003d90ae4c8386a5b35e3d712f78,
title = "Photometric redshift estimation: An active learning approach",
keywords = "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",
author = "R. Vilalta and Ishida, \{E. E. O.\} and R. Beck and R. Sutrisno and \{de Souza\}, R.\textasciitilde{}S. and A. Mahabal",
year = "2018",
month = feb,
day = "8",
doi = "10.1109/SSCI.2017.8285192",
language = "English",
isbn = "9781538627273",
series = "IEEE Symposium Series on Computational Intelligence (SSCI)",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "1--8",
booktitle = "2017 IEEE Symposium Series on Computational Intelligence (SSCI)",
}