160 citations · 380 across the 6 of their papers we have counts for
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astro-ph.IM2012★ 38 cited
Can Self-Organizing Maps accurately predict photometric redshifts?
M. J. Way, C. D. Klose
We present an unsupervised machine learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization approach calle…
astro-ph.IM2010★ 7 cited
Viewpoints: A high-performance high-dimensional exploratory data analysis tool
P. R. Gazis, C. Levit, M. J. Way
Scientific data sets continue to increase in both size and complexity. In the past, dedicated graphics systems at supercomputing centers were required to visualize large data sets,…
astro-ph.IM2009★ 19 cited
The Revolution in Astronomy Education: Data Science for the Masses
Kirk D. Borne, Suzanne Jacoby, K. Carney +5
As our capacity to study ever-expanding domains of our science has increased (including the time domain, non-electromagnetic phenomena, magnetized plasmas, and numerous sky surveys…