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pro vyhledávání: '"Venturini, A."'
Autor:
Franco Maria Nardini, Nicola Tonellotto, Rossano Venturini, Salvatore Orlando, Claudio Lucchese, Raffaele Perego
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783319712727
ECML/PKDD (3)
ECML PKDD-Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 383–387, Skopje, Macedonia, 18-22 September, 2017
info:cnr-pdr/source/autori:Lucchese C.; Nardini F.M.; Orlando S.; Perego R.; Tonellotto N.; Venturini R./congresso_nome:ECML PKDD-Joint European Conference on Machine Learning and Knowledge Discovery in Databases/congresso_luogo:Skopje, Macedonia/congresso_data:18-22 September, 2017/anno:2017/pagina_da:383/pagina_a:387/intervallo_pagine:383–387
ECML/PKDD (3)
ECML PKDD-Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 383–387, Skopje, Macedonia, 18-22 September, 2017
info:cnr-pdr/source/autori:Lucchese C.; Nardini F.M.; Orlando S.; Perego R.; Tonellotto N.; Venturini R./congresso_nome:ECML PKDD-Joint European Conference on Machine Learning and Knowledge Discovery in Databases/congresso_luogo:Skopje, Macedonia/congresso_data:18-22 September, 2017/anno:2017/pagina_da:383/pagina_a:387/intervallo_pagine:383–387
Machine-learnt models based on additive ensembles of binary regression trees are currently deemed the best solution to address complex classification, regression, and ranking tasks. Evaluating these models is a computationally demanding task as it ne
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::95552ec89c705917a658185b4a9192d7
http://hdl.handle.net/11568/945058
http://hdl.handle.net/11568/945058