Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Diego Fustes"'
Autor:
Iván Froiz-Míguez, Óscar Blanco-Novoa, Paula Fraga-Lamas, Diego Fustes, José Carlos Dafonte Vázquez, Javier Pereira, Tiago M. Fernández-Caramés
Publikováno v:
Kalpa Publications in Computing.
In recent years Machine Learning (ML) strategies have proven to be useful to automate numerous classification and pattern detection tasks in diverse fields thanks to the increase of computational power in hardware. One of such fields is the Automatic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4f7b00921ff65c72c0aee2d8c29ed485
https://doi.org/10.29007/1ppr
https://doi.org/10.29007/1ppr
Autor:
Carmelo del Coso, Bernardino Arcay, José M. Rodríguez-Pedreira, Carlos Dafonte, Francisco J. Novoa, Diego Fustes
Publikováno v:
Applied Soft Computing. 36:246-254
Graphical abstractDisplay Omitted HighlightsSelf-Organizing Maps (SOMs) are powerful tools with many applications. Nevertheless, they cannot deal directly with categorical variables.In order to present categorical variables to SOMs, they are usually
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dfccc0551c3cd865158e03128b9502db
https://doi.org/10.1016/j.asoc.2015.06.058
https://doi.org/10.1016/j.asoc.2015.06.058
Autor:
Diego Fustes, Alfonso Iglesias, Minia Manteiga, Diego Cantorna, Bernardino Arcay, Carlos Dafonte
Publikováno v:
Future Generation Computer Systems. 34:155-160
Geographic Information Systems (GIS) have gained popularity in recent years because they provide spatial data management and access through the Web. This article gives a detailed description of a tool that offers an integrated framework for the detec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59509cf23e9df78ec16f6cbbc723e543
https://doi.org/10.1016/j.future.2013.09.020
https://doi.org/10.1016/j.future.2013.09.020
Autor:
Antonella Vallenari, Xavier Luri, Bernardino Arcay, Carlos Dafonte, K. W. Smith, Minia Manteiga, Diego Fustes
Publikováno v:
Expert Systems with Applications. 40:1530-1541
Gaia is an ESA cornerstone astronomical mission that will observe with unprecedented precision positions, distances, space motions, and many physical properties of more than one billion objects in our Galaxy and beyond. It will observe all objects in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e58deac51630545bc8d63bd6a62b4cb
https://doi.org/10.1016/j.eswa.2012.08.069
https://doi.org/10.1016/j.eswa.2012.08.069
Publikováno v:
IFSA-EUSFLAT
Since its launch in December 2013, the Gaia space mission has collected and continues to collect tremendous amounts of information concerning the objects that populate our Galaxy and beyond. The international Gaia Data and Analysis Consortium (DPAC)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c08063b3a3a3b14d6d98c10de9f72f5c
https://doi.org/10.2991/ifsa-eusflat-15.2015.126
https://doi.org/10.2991/ifsa-eusflat-15.2015.126
Autor:
Ana Ulla, C. Allende Prieto, Diego Fustes, D. Garabato, Carlos Dafonte, Minia Manteiga, M. A. Álvarez
Publikováno v:
Astronomy & Astrophysics. 594:A68
Aims. We present an innovative artificial neural network (ANN) architecture, called Generative ANN (GANN), that computes the forward model, that is it learns the function that relates the unknown outputs (stellar atmospheric parameters, in this case)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a49c263d79c52c0a268d573fbf9d94d1
https://doi.org/10.1051/0004-6361/201527045
https://doi.org/10.1051/0004-6361/201527045
Publikováno v:
Lecture Notes in Electrical Engineering ISBN: 9783642276378
This chapter focuses on how to monitor marine spills using powerful tools such as remote sensing and Geographic Information Systems (GIS). On the one hand, remote sensing has been widely used as one of the main ways to periodically monitor large area
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9eb4485a85b579c8d349e9e81b5577d5
https://doi.org/10.1007/978-3-642-27638-5_15
https://doi.org/10.1007/978-3-642-27638-5_15
Autor:
Diego Cantorna, Minia Manteiga, Alfonso Iglesias, Bernardino Arcay, Carlos Dafonte, Diego Fustes
Publikováno v:
Ubiquitous Computing and Ambient Intelligence ISBN: 9783642353765
UCAmI
UCAmI
Geographic Information Systems (GIS) have gained popularity in recent years because they provide spatial data management and access through the Web. This article gives a detailed description of a tool that offers an integrated framework for the detec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d2b3a222a083635586242a4f3d7b8e2
https://doi.org/10.1007/978-3-642-35377-2_62
https://doi.org/10.1007/978-3-642-35377-2_62
Publikováno v:
Astrostatistics and Data Mining ISBN: 9781461433224
This work presents an algorithm that was developed to select the most relevant areas of a stellar spectrum to extract its basic atmospheric parameters. We consider synthetic spectra obtained from models of stellar atmospheres in the spectral region o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b091dc749ff18babc236f15e4433748c
https://doi.org/10.1007/978-1-4614-3323-1_12
https://doi.org/10.1007/978-1-4614-3323-1_12
Publikováno v:
EAS Publications Series. :373-373
We present a method for knowledge analysis in large astronomical spectrophotometric archives. The method is based on a type of unsupervised learning Artificial Neural Networks named Self-organizing maps (SOMs). SOMs are used to organize the informati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d649e6a7e12a052acbf2b7bbcbc32f6e
https://doi.org/10.1051/eas/1567073
https://doi.org/10.1051/eas/1567073