Data Mining and Machine Learning in Astronomy

no code implementations • 11 Jun 2009 • Nicholas M. Ball, Robert J. Brunner

We review the current state of data mining and machine learning in astronomy.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics

Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX

no code implementations • 21 Apr 2008 • Nicholas M. Ball, Robert J. Brunner, Adam D. Myers, Natalie E. Strand, Stacey L. Alberts, David Tcheng

We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS DR5).

Robust Machine Learning Applied to Astronomical Datasets II: Quantifying Photometric Redshifts for Quasars Using Instance-Based Learning

no code implementations • 17 Dec 2006 • Nicholas M. Ball, Robert J. Brunner, Adam D. Myers, Natalie E. Strand, Stacey L. Alberts, David Tcheng, Xavier Llorà

The instance-based algorithm is trained on a representative sample of the data and pseudo-blind-tested on the remaining unseen data.

astro-ph