Machine Learning and Knowledge Discovery in Databases, 1st ed. 2015 European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I Lecture Notes in Artificial Intelligence Series
Coordonnateurs : Appice Annalisa, Rodrigues Pedro Pereira, Santos Costa Vítor, Soares Carlos, Gama João, Jorge Alípio
The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015.
The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.
Includes supplementary material: sn.pub/extras
Date de parution : 09-2015
Ouvrage de 709 p.
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 52,74 €
Ajouter au panierThèmes de Machine Learning and Knowledge Discovery in Databases :
Mots-clés :
data mining; foundations of machine learning and data mining; knowledge discovery in databases; probabilistic models and statistical methods; social and graphs mining; classification; regression and supervised learning; clustering and unsupervised learning; domain adaptation; ensemble learning; large scale learning and big data; learning paradigms; machine learning and data mining applications; machine learning methodologies; meta-learning; nonmonotonic constraints; pattern and sequence mining; privacy-preservi