Feature importance for machine learning redshifts applied to SDSS galaxies

Monthly Notices of the Royal Astronomical Society

Volumn 449, Issue 2, 2015, Pages 1275-1283

  Hoyle, Ben ^a,b   Michael Rau, Markus ^a   Zitlau, Roman ^a   Seitz, Stella ^a,c   Weller, Jochen ^a,b,c  

^a UNIVERSITY OF MUNICH   (Germany)

^b EXCELLENCE CLUSTER UNIVERSE   (Germany)

^c MAX PLANCK INSTITUT FÜR EXTRATERRESTRISCHE PHYSIK   (Germany)

Author keywords

Catalogues; Galaxies: distances and redshifts; Surveys

Indexed keywords

ADAPTIVE BOOSTING; DECISION TREES; FEATURE EXTRACTION; GALAXIES; MACHINE LEARNING; NEURAL NETWORKS; PHOTOMETRY; STATISTICS;

ARCHITECTURE DECISIONS; CATALOG; ENSEMBLE LEARNING; FEATURES SELECTION; GALAXIES DISTANCES AND REDSHIFTS; LEARNING ARCHITECTURES; PHOTOMETRICS; RED SHIFT; SLOAN DIGITAL SKY SURVEY; SURVEYS-GALAXIES;

SURVEYS;

EID: 85017213288     PISSN: 00358711     EISSN: 13652966     Source Type: Journal    
DOI: 10.1093/mnras/stv373     Document Type: Article

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