Downloads provided by UsageCounts
Data from the SPLUS survey have been cross-matched with GALEX, SDSS, GAIA, 2MASS and WISE with the aim to characterize Hα sources. The study was focused on Hα emitters and their link with UV emitters. Machine learning algorithms were also employed for the separation of Hα emitters in S-PLUS survey of UV and non-UV sources based on GALEX.
machine learning, prediction models, Hα emmiters, diagnosstic diagrams, photometric data, random forest, classification tree
machine learning, prediction models, Hα emmiters, diagnosstic diagrams, photometric data, random forest, classification tree
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 36 | |
| downloads | 31 |

Views provided by UsageCounts
Downloads provided by UsageCounts