Name
Abstract | ||
|---|---|---|
Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use of this new approach. The usage of machine learning methods, however is still far from trivial and many problems still need to be solved. Using the evaluation of photometric redshifts as a case study, we outline the main problems and some ongoing efforts to solve them. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-96553-6_5 | DAMDID/RCDL (Selected Papers) |
Field | DocType | ISSN |
Astronomy,Data science,Redshift,Photometry (optics),Astroinformatics,Sextant (astronomical),Big data,Physics | Conference | 2018 Communications in Computer and Information Science, vol 822.
Springer, Cham |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Massimo Brescia | 1 | 14 | 8.41 |
| Stefano Cavuoti | 2 | 10 | 4.79 |
| Valeria Amaro | 3 | 0 | 1.01 |
| giuseppe riccio | 4 | 4 | 3.21 |
| Giuseppe Angora | 5 | 0 | 0.68 |
| Civita Vellucci | 6 | 0 | 0.68 |
| Giuseppe Longo | 7 | 78 | 16.22 |