Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12386/27591
DC Field | Value | Language |
---|---|---|
dc.contributor.author | ANGORA, GIUSEPPE | en_US |
dc.contributor.author | BRESCIA, Massimo | en_US |
dc.contributor.author | CAVUOTI, STEFANO | en_US |
dc.contributor.author | RICCIO, GIUSEPPE | en_US |
dc.contributor.author | Paolillo, Maurizio | en_US |
dc.contributor.author | Puzia, Thomas H. | en_US |
dc.date.accessioned | 2020-10-05T14:34:06Z | - |
dc.date.available | 2020-10-05T14:34:06Z | - |
dc.date.issued | 2018 | en_US |
dc.identifier.isbn | 978-3-319-96552-9 | en_US |
dc.identifier.issn | 1865-0929 | en_US |
dc.identifier.uri | http://hdl.handle.net/20.500.12386/27591 | - |
dc.description.abstract | Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multi-dimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts.... | en_US |
dc.language.iso | eng | en_US |
dc.title | Neural Gas based classification of Globular Clusters | en_US |
dc.type | Book part | - |
dc.identifier.doi | https://doi.org/10.1007/978-3-319-57135-5 | en_US |
dc.identifier.scopus | 2-s2.0-85050398009 | en_US |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-319-96553-6_7 | en_US |
dc.identifier.url | https://damdid2017.frccsc.ru/en/conference_short.html | en_US |
dc.relation.medium | STAMPA | en_US |
dc.relation.volume | 822 | en_US |
dc.type.referee | REF_1 | en_US |
dc.description.numberofauthors | 6 | en_US |
dc.description.international | sì | en_US |
dc.contributor.country | ITA | en_US |
dc.contributor.country | CHL | en_US |
dc.relation.alleditors | Kalinichenko, Leonid; Manolopoulos, Yannis; Malkov, Oleg; Skvortsov, Nikolay; Stupnikov, Sergey; Sukhomlin, Vladimir | en_US |
dc.relation.ispartofbook | Data Analytics and Management in Data Intensive Domains XIX International Conference, DAMDID/RCDL 2017 | en_US |
dc.type.invited | no | en_US |
dc.relation.scientificsector | FIS/05 - ASTRONOMIA E ASTROFISICA | en_US |
dc.type.miur | 268 Contributo in volume (Capitolo o Saggio) | - |
dc.relation.series | COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE | en_US |
dc.identifier.adsbibcode | 2018arXiv180208086A | en_US |
dc.relation.ercsector | ERC sectors::Physical Sciences and Engineering::PE9 Universe sciences: astro-physics/chemistry/biology; solar systems; stellar, galactic and extragalactic astronomy, planetary systems, cosmology, space science, instrumentation | en_US |
dc.description.apc | no | en_US |
dc.description.oa | 1 – prodotto con file in versione Open Access (allegare il file al passo 5-Carica) | en_US |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.openairetype | Book part | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.fulltext | With Fulltext | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.dept | O.A. Capodimonte | - |
crisitem.author.orcid | 0000-0001-9506-5680 | - |
crisitem.author.orcid | 0000-0002-3787-4196 | - |
crisitem.author.orcid | 0000-0001-7020-1172 | - |
crisitem.author.orcid | 0000-0003-4210-7693 | - |
crisitem.journal.journalissn | 1865-0929 | - |
crisitem.journal.ance | E211396 | - |
Appears in Collections: | 2.01 Capitoli o saggi in libro |
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File | Description | Size | Format | |
---|---|---|---|---|
AngoraEtAl-1802.08086.pdf | postprint | 959.79 kB | Adobe PDF | View/Open |
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